diff --git a/lecture_material/22-classification/22-classification.ipynb b/lecture_material/22-classification/22-classification.ipynb
index b14d7fc99862070b48c3035d084689280074573b..8f01f8ac6efc84edf27e000b2a4b58a788703e67 100644
--- a/lecture_material/22-classification/22-classification.ipynb
+++ b/lecture_material/22-classification/22-classification.ipynb
@@ -31,6 +31,17 @@
     "from sklearn.metrics import recall_score, precision_score, balanced_accuracy_score"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "67406ac6-bd4a-4d07-bd2c-abf5db4122ce",
+   "metadata": {},
+   "source": [
+    "### IRIS dataset: http://archive.ics.uci.edu/ml/datasets/iris\n",
+    "- This set of data is used in beginning Machine Learning Courses\n",
+    "- You can train a ML algorithm to use the values to predict the class of iris\n",
+    "- Dataset link: https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 2,
@@ -208,7 +219,6 @@
     }
    ],
    "source": [
-    "xcols = [\"sepal length (cm)\", \"sepal width (cm)\", \"const\"]\n",
     "train, test = train_test_split(df, test_size=10, random_state=5)\n",
     "test"
    ]
@@ -439,7 +449,7 @@
     {
      "data": {
       "text/plain": [
-       "0.5805709195672956"
+       "0.5805709195672948"
       ]
      },
      "execution_count": 5,
@@ -873,9 +883,9 @@
     {
      "data": {
       "text/plain": [
-       "array([[-2.42108094],\n",
-       "       [ 3.90002306],\n",
-       "       [ 0.75064911]])"
+       "array([[-2.42109577],\n",
+       "       [ 3.90001685],\n",
+       "       [ 0.75073163]])"
       ]
      },
      "execution_count": 13,
@@ -947,16 +957,16 @@
     {
      "data": {
       "text/plain": [
-       "array([[-2.76155804],\n",
-       "       [-3.87788463],\n",
-       "       [-2.37155574],\n",
-       "       [ 2.5779668 ],\n",
-       "       [-2.6410506 ],\n",
-       "       [-1.75259212],\n",
-       "       [ 0.58742172],\n",
-       "       [-1.05365461],\n",
-       "       [ 0.34531362],\n",
-       "       [-0.62311875]])"
+       "array([[-2.76157837],\n",
+       "       [-3.87790879],\n",
+       "       [-2.37157668],\n",
+       "       [ 2.57796414],\n",
+       "       [-2.64109729],\n",
+       "       [-1.75259723],\n",
+       "       [ 0.58741288],\n",
+       "       [-1.05367952],\n",
+       "       [ 0.34530331],\n",
+       "       [-0.623135  ]])"
       ]
      },
      "execution_count": 16,
@@ -1070,16 +1080,16 @@
     {
      "data": {
       "text/plain": [
-       "array([[0.9405628 , 0.0594372 ],\n",
-       "       [0.97972503, 0.02027497],\n",
-       "       [0.91463241, 0.08536759],\n",
-       "       [0.07056997, 0.92943003],\n",
-       "       [0.93345725, 0.06654275],\n",
-       "       [0.85227945, 0.14772055],\n",
-       "       [0.35722665, 0.64277335],\n",
-       "       [0.74147607, 0.25852393],\n",
-       "       [0.41451931, 0.58548069],\n",
-       "       [0.65092753, 0.34907247]])"
+       "array([[0.94056393, 0.05943607],\n",
+       "       [0.97972551, 0.02027449],\n",
+       "       [0.91463405, 0.08536595],\n",
+       "       [0.07057015, 0.92942985],\n",
+       "       [0.93346015, 0.06653985],\n",
+       "       [0.85228009, 0.14771991],\n",
+       "       [0.35722868, 0.64277132],\n",
+       "       [0.74148084, 0.25851916],\n",
+       "       [0.41452182, 0.58547818],\n",
+       "       [0.65093122, 0.34906878]])"
       ]
      },
      "execution_count": 19,
@@ -1108,8 +1118,8 @@
     {
      "data": {
       "text/plain": [
-       "array([0.0594372 , 0.02027497, 0.08536759, 0.92943003, 0.06654275,\n",
-       "       0.14772055, 0.64277335, 0.25852393, 0.58548069, 0.34907247])"
+       "array([0.05943607, 0.02027449, 0.08536595, 0.92942985, 0.06653985,\n",
+       "       0.14771991, 0.64277132, 0.25851916, 0.58547818, 0.34906878])"
       ]
      },
      "execution_count": 20,
@@ -1170,7 +1180,7 @@
        "      <td>versicolor</td>\n",
        "      <td>1.327769</td>\n",
        "      <td>False</td>\n",
-       "      <td>0.059437</td>\n",
+       "      <td>0.059436</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>134</th>\n",
@@ -1182,7 +1192,7 @@
        "      <td>virginica</td>\n",
        "      <td>1.590835</td>\n",
        "      <td>False</td>\n",
-       "      <td>0.020275</td>\n",
+       "      <td>0.020274</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>114</th>\n",
@@ -1194,7 +1204,7 @@
        "      <td>virginica</td>\n",
        "      <td>1.279061</td>\n",
        "      <td>False</td>\n",
-       "      <td>0.085368</td>\n",
+       "      <td>0.085366</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>42</th>\n",
@@ -1218,7 +1228,7 @@
        "      <td>virginica</td>\n",
        "      <td>1.889735</td>\n",
        "      <td>False</td>\n",
-       "      <td>0.066543</td>\n",
+       "      <td>0.066540</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>57</th>\n",
@@ -1230,7 +1240,7 @@
        "      <td>versicolor</td>\n",
        "      <td>0.830818</td>\n",
        "      <td>False</td>\n",
-       "      <td>0.147721</td>\n",
+       "      <td>0.147720</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
@@ -1242,7 +1252,7 @@
        "      <td>setosa</td>\n",
        "      <td>0.538569</td>\n",
        "      <td>True</td>\n",
-       "      <td>0.642773</td>\n",
+       "      <td>0.642771</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>70</th>\n",
@@ -1254,7 +1264,7 @@
        "      <td>versicolor</td>\n",
        "      <td>1.155681</td>\n",
        "      <td>False</td>\n",
-       "      <td>0.258524</td>\n",
+       "      <td>0.258519</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25</th>\n",
@@ -1266,7 +1276,7 @@
        "      <td>setosa</td>\n",
        "      <td>0.610022</td>\n",
        "      <td>True</td>\n",
-       "      <td>0.585481</td>\n",
+       "      <td>0.585478</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>84</th>\n",
@@ -1278,7 +1288,7 @@
        "      <td>versicolor</td>\n",
        "      <td>0.895833</td>\n",
        "      <td>False</td>\n",
-       "      <td>0.349072</td>\n",
+       "      <td>0.349069</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1298,16 +1308,16 @@
        "84                 5.4               3.0      1               1.5   False   \n",
        "\n",
        "        variety  pet_width_predictions  setosa_predictions  setosa_prob  \n",
-       "82   versicolor               1.327769               False     0.059437  \n",
-       "134   virginica               1.590835               False     0.020275  \n",
-       "114   virginica               1.279061               False     0.085368  \n",
+       "82   versicolor               1.327769               False     0.059436  \n",
+       "134   virginica               1.590835               False     0.020274  \n",
+       "114   virginica               1.279061               False     0.085366  \n",
        "42       setosa               0.083889                True     0.929430  \n",
-       "109   virginica               1.889735               False     0.066543  \n",
-       "57   versicolor               0.830818               False     0.147721  \n",
-       "1        setosa               0.538569                True     0.642773  \n",
-       "70   versicolor               1.155681               False     0.258524  \n",
-       "25       setosa               0.610022                True     0.585481  \n",
-       "84   versicolor               0.895833               False     0.349072  "
+       "109   virginica               1.889735               False     0.066540  \n",
+       "57   versicolor               0.830818               False     0.147720  \n",
+       "1        setosa               0.538569                True     0.642771  \n",
+       "70   versicolor               1.155681               False     0.258519  \n",
+       "25       setosa               0.610022                True     0.585478  \n",
+       "84   versicolor               0.895833               False     0.349069  "
       ]
      },
      "execution_count": 21,
@@ -1337,7 +1347,7 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f1b2e0c37f0>]"
+       "[<matplotlib.lines.Line2D at 0x7a9f1a7435e0>]"
       ]
      },
      "execution_count": 22,
@@ -1346,7 +1356,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1381,16 +1391,16 @@
     {
      "data": {
       "text/plain": [
-       "array([[0.0594372 ],\n",
-       "       [0.02027497],\n",
-       "       [0.08536759],\n",
-       "       [0.92943003],\n",
-       "       [0.06654275],\n",
-       "       [0.14772055],\n",
-       "       [0.64277335],\n",
-       "       [0.25852393],\n",
-       "       [0.58548069],\n",
-       "       [0.34907247]])"
+       "array([[0.05943607],\n",
+       "       [0.02027449],\n",
+       "       [0.08536595],\n",
+       "       [0.92942985],\n",
+       "       [0.06653985],\n",
+       "       [0.14771991],\n",
+       "       [0.64277132],\n",
+       "       [0.25851916],\n",
+       "       [0.58547818],\n",
+       "       [0.34906878]])"
       ]
      },
      "execution_count": 23,
@@ -1412,16 +1422,16 @@
     {
      "data": {
       "text/plain": [
-       "82     0.059437\n",
-       "134    0.020275\n",
-       "114    0.085368\n",
+       "82     0.059436\n",
+       "134    0.020274\n",
+       "114    0.085366\n",
        "42     0.929430\n",
-       "109    0.066543\n",
-       "57     0.147721\n",
-       "1      0.642773\n",
-       "70     0.258524\n",
-       "25     0.585481\n",
-       "84     0.349072\n",
+       "109    0.066540\n",
+       "57     0.147720\n",
+       "1      0.642771\n",
+       "70     0.258519\n",
+       "25     0.585478\n",
+       "84     0.349069\n",
        "Name: setosa_prob, dtype: float64"
       ]
      },
@@ -1495,7 +1505,7 @@
        "      <td>versicolor</td>\n",
        "      <td>1.327769</td>\n",
        "      <td>False</td>\n",
-       "      <td>0.059437</td>\n",
+       "      <td>0.059436</td>\n",
        "      <td>versicolor</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1508,7 +1518,7 @@
        "      <td>virginica</td>\n",
        "      <td>1.590835</td>\n",
        "      <td>False</td>\n",
-       "      <td>0.020275</td>\n",
+       "      <td>0.020274</td>\n",
        "      <td>virginica</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1521,7 +1531,7 @@
        "      <td>virginica</td>\n",
        "      <td>1.279061</td>\n",
        "      <td>False</td>\n",
-       "      <td>0.085368</td>\n",
+       "      <td>0.085366</td>\n",
        "      <td>versicolor</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1547,7 +1557,7 @@
        "      <td>virginica</td>\n",
        "      <td>1.889735</td>\n",
        "      <td>False</td>\n",
-       "      <td>0.066543</td>\n",
+       "      <td>0.066540</td>\n",
        "      <td>virginica</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1560,7 +1570,7 @@
        "      <td>versicolor</td>\n",
        "      <td>0.830818</td>\n",
        "      <td>False</td>\n",
-       "      <td>0.147721</td>\n",
+       "      <td>0.147720</td>\n",
        "      <td>versicolor</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1573,7 +1583,7 @@
        "      <td>setosa</td>\n",
        "      <td>0.538569</td>\n",
        "      <td>True</td>\n",
-       "      <td>0.642773</td>\n",
+       "      <td>0.642771</td>\n",
        "      <td>setosa</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1586,7 +1596,7 @@
        "      <td>versicolor</td>\n",
        "      <td>1.155681</td>\n",
        "      <td>False</td>\n",
-       "      <td>0.258524</td>\n",
+       "      <td>0.258519</td>\n",
        "      <td>versicolor</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1599,7 +1609,7 @@
        "      <td>setosa</td>\n",
        "      <td>0.610022</td>\n",
        "      <td>True</td>\n",
-       "      <td>0.585481</td>\n",
+       "      <td>0.585478</td>\n",
        "      <td>setosa</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1612,7 +1622,7 @@
        "      <td>versicolor</td>\n",
        "      <td>0.895833</td>\n",
        "      <td>False</td>\n",
-       "      <td>0.349072</td>\n",
+       "      <td>0.349069</td>\n",
        "      <td>versicolor</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -1633,16 +1643,16 @@
        "84                 5.4               3.0      1               1.5   False   \n",
        "\n",
        "        variety  pet_width_predictions  setosa_predictions  setosa_prob  \\\n",
-       "82   versicolor               1.327769               False     0.059437   \n",
-       "134   virginica               1.590835               False     0.020275   \n",
-       "114   virginica               1.279061               False     0.085368   \n",
+       "82   versicolor               1.327769               False     0.059436   \n",
+       "134   virginica               1.590835               False     0.020274   \n",
+       "114   virginica               1.279061               False     0.085366   \n",
        "42       setosa               0.083889                True     0.929430   \n",
-       "109   virginica               1.889735               False     0.066543   \n",
-       "57   versicolor               0.830818               False     0.147721   \n",
-       "1        setosa               0.538569                True     0.642773   \n",
-       "70   versicolor               1.155681               False     0.258524   \n",
-       "25       setosa               0.610022                True     0.585481   \n",
-       "84   versicolor               0.895833               False     0.349072   \n",
+       "109   virginica               1.889735               False     0.066540   \n",
+       "57   versicolor               0.830818               False     0.147720   \n",
+       "1        setosa               0.538569                True     0.642771   \n",
+       "70   versicolor               1.155681               False     0.258519   \n",
+       "25       setosa               0.610022                True     0.585478   \n",
+       "84   versicolor               0.895833               False     0.349069   \n",
        "\n",
        "    variety_predictions  \n",
        "82           versicolor  \n",
@@ -1722,9 +1732,9 @@
     {
      "data": {
       "text/plain": [
-       "array([[-1.80097204,  3.01989635,  0.69404974],\n",
-       "       [ 0.64875706, -1.46308696,  1.04354373],\n",
-       "       [ 1.15221498, -1.55680939, -1.73759347]])"
+       "array([[-1.8013236 ,  3.0203917 ,  0.6943858 ],\n",
+       "       [ 0.64905246, -1.46381322,  1.04399268],\n",
+       "       [ 1.15227114, -1.55657848, -1.73837849]])"
       ]
      },
      "execution_count": 27,
@@ -1754,9 +1764,9 @@
     {
      "data": {
       "text/plain": [
-       "array([[-1.80097204,  0.64875706,  1.15221498],\n",
-       "       [ 3.01989635, -1.46308696, -1.55680939],\n",
-       "       [ 0.69404974,  1.04354373, -1.73759347]])"
+       "array([[-1.8013236 ,  0.64905246,  1.15227114],\n",
+       "       [ 3.0203917 , -1.46381322, -1.55657848],\n",
+       "       [ 0.6943858 ,  1.04399268, -1.73837849]])"
       ]
      },
      "execution_count": 28,
@@ -1884,9 +1894,9 @@
     {
      "data": {
       "text/plain": [
-       "array([[-1.80097204],\n",
-       "       [ 3.01989635],\n",
-       "       [ 0.69404974]])"
+       "array([[-1.8013236],\n",
+       "       [ 3.0203917],\n",
+       "       [ 0.6943858]])"
       ]
      },
      "execution_count": 32,
@@ -1917,16 +1927,16 @@
     {
      "data": {
       "text/plain": [
-       "array([[-1.59786793],\n",
-       "       [-2.44014918],\n",
-       "       [-1.2958783 ],\n",
-       "       [ 2.43344109],\n",
-       "       [-1.40132207],\n",
-       "       [-0.88296201],\n",
-       "       [ 0.9289758 ],\n",
-       "       [-0.26801696],\n",
-       "       [ 0.7488786 ],\n",
-       "       [ 0.02848978]])"
+       "array([[-1.59823349],\n",
+       "       [-2.44066973],\n",
+       "       [-1.29619432],\n",
+       "       [ 2.4338154 ],\n",
+       "       [-1.401734  ],\n",
+       "       [-0.88315976],\n",
+       "       [ 0.92907526],\n",
+       "       [-0.26817   ],\n",
+       "       [ 0.7489429 ],\n",
+       "       [ 0.02841346]])"
       ]
      },
      "execution_count": 33,
@@ -1955,16 +1965,16 @@
     {
      "data": {
       "text/plain": [
-       "array([[ 0.85599987],\n",
-       "       [ 1.19693568],\n",
-       "       [ 0.70969118],\n",
-       "       [-0.78380349],\n",
-       "       [ 0.44748149],\n",
-       "       [ 0.71104461],\n",
-       "       [-0.16680757],\n",
-       "       [ 0.1893321 ],\n",
-       "       [-0.10193186],\n",
-       "       [ 0.15757096]])"
+       "array([[ 0.85620125],\n",
+       "       [ 1.19729831],\n",
+       "       [ 0.70981993],\n",
+       "       [-0.7843788 ],\n",
+       "       [ 0.4474428 ],\n",
+       "       [ 0.711198  ],\n",
+       "       [-0.16708993],\n",
+       "       [ 0.18919989],\n",
+       "       [-0.10218468],\n",
+       "       [ 0.1574363 ]])"
       ]
      },
      "execution_count": 34,
@@ -1987,16 +1997,16 @@
     {
      "data": {
       "text/plain": [
-       "array([[ 0.74186806],\n",
-       "       [ 1.24321349],\n",
-       "       [ 0.58618712],\n",
-       "       [-1.6496376 ],\n",
-       "       [ 0.95384058],\n",
-       "       [ 0.1719174 ],\n",
-       "       [-0.76216824],\n",
-       "       [ 0.07868487],\n",
-       "       [-0.64694674],\n",
-       "       [-0.18606075]])"
+       "array([[ 0.74203223],\n",
+       "       [ 1.24337142],\n",
+       "       [ 0.58637439],\n",
+       "       [-1.6494366 ],\n",
+       "       [ 0.9542912 ],\n",
+       "       [ 0.17196175],\n",
+       "       [-0.76198533],\n",
+       "       [ 0.07897011],\n",
+       "       [-0.64675822],\n",
+       "       [-0.18584976]])"
       ]
      },
      "execution_count": 35,
@@ -2040,16 +2050,16 @@
     {
      "data": {
       "text/plain": [
-       "array([[-1.59786793,  0.85599987,  0.74186806],\n",
-       "       [-2.44014918,  1.19693568,  1.24321349],\n",
-       "       [-1.2958783 ,  0.70969118,  0.58618712],\n",
-       "       [ 2.43344109, -0.78380349, -1.6496376 ],\n",
-       "       [-1.40132207,  0.44748149,  0.95384058],\n",
-       "       [-0.88296201,  0.71104461,  0.1719174 ],\n",
-       "       [ 0.9289758 , -0.16680757, -0.76216824],\n",
-       "       [-0.26801696,  0.1893321 ,  0.07868487],\n",
-       "       [ 0.7488786 , -0.10193186, -0.64694674],\n",
-       "       [ 0.02848978,  0.15757096, -0.18606075]])"
+       "array([[-1.59823349,  0.85620125,  0.74203223],\n",
+       "       [-2.44066973,  1.19729831,  1.24337142],\n",
+       "       [-1.29619432,  0.70981993,  0.58637439],\n",
+       "       [ 2.4338154 , -0.7843788 , -1.6494366 ],\n",
+       "       [-1.401734  ,  0.4474428 ,  0.9542912 ],\n",
+       "       [-0.88315976,  0.711198  ,  0.17196175],\n",
+       "       [ 0.92907526, -0.16708993, -0.76198533],\n",
+       "       [-0.26817   ,  0.18919989,  0.07897011],\n",
+       "       [ 0.7489429 , -0.10218468, -0.64675822],\n",
+       "       [ 0.02841346,  0.1574363 , -0.18584976]])"
       ]
      },
      "execution_count": 36,
@@ -2561,7 +2571,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.contour.QuadContourSet at 0x7f1b2bf50340>"
+       "<matplotlib.contour.QuadContourSet at 0x7a9f186cc550>"
       ]
      },
      "execution_count": 52,
@@ -2570,7 +2580,7 @@
     },
     {
      "data": {
-      "image/png": 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PRoEvcJyCZLDhgF1sOcwgNgAEGscqsIujFecRG/ANthtIBcEBu4gO5xAb8BWCA6kgOJAMoiN1xAaAUOFYBckiOJJHbMB32G7ACQQHksGWIznEBnyJ4IATCA4ki+iwh9gAEGocqyAVREf3EBvwLbYbcBLBgVQQHSdGbMDXCA44ieBAqoiOzhEbAPAdHKvACURHR8QGfI/tBkwgOOAEouM/iA0EAsEBEwgOOCXs0UFsAMAJcKwCJ4U1OogNBAbbDZhEcMBJYYsOYgOBQnDAJIIDTjsWHUEPD2IDAGzgWAWmBDk6iA0EDtsNpAPBAVOCGB3EBgKJ4EA6EBwwKUjRQWwAQAo4VoFpQYgOYgOBxXYD6URwwDQ/RwexgUAjOJBOBAfSwY/Rke32AAAQJMeC46shWS5PgqD7bnAcLj/s4iQnx2YDgcd2A25gy4F08vq2g9hAKBAccAM3jyLdvBodxAYAGEZwIN28Fh3EBkKD7QbcRHDADV6JDmIDoUJwwE0cq8AtbkcHsYHQITjgNoIDbnErOogNAHABwQE3pfvTZm3Fxj333KOMjIwOV3l5uanZAGPYbsALOFaBF6QjOmz/Ua+hQ4fqrbfe+u83yObvgsGf8pv2qXl0H7fHAFT0YSt/BAyuOxYcJv5AmO1SyM7O1plnnun4IAAQZgQHvMJEdNi+Z+OTTz5RSUmJBgwYoIqKCu3Zs+eEz08kEorH4x0uwCs4ToGXcKwCL3HyeMVWbIwbN07PPPOM3nzzTdXW1mrXrl264IIL1Nzc3OVrampqVFhY2H5Fo9GUhwacRHDAawgOeMmx6Ih8knx4ZFiWZSX74oMHD6qsrEyPPPKIbrzxxk6fk0gklEgk2r+Ox+OKRqOaHJ2t7MxIsm8NOI77N+BFHK3AK1oTh/XJb36pWCymgoICW69N6e7O0047TYMGDdLOnTu7fE4kElEkQlTA+7hhFF7Ep8giCFL6OxuHDh3Sp59+quLiYqfmAVzFkQq8ivs54Ge2YuP222/Xhg0b9Pnnn+vtt9/WlVdeqaysLF1zzTWm5gPSjuCAlxEd8CNbsfH3v/9d11xzjQYPHqyrrrpKRUVF2rRpk04//XRT8wGuIDjgdQQH/MTWPRsrV640NQfgOdzDAa/jfg74BZ+NApwAGw74AUcr8DpiAzgJggN+QXTAq4gNoBsIDvgJ0QGvITaAbiI44DdEB7yC2ABsIDjgR0QH3EZsADYRHPArogNuITaAJBAc8DOCA+lGbABJIjjgZ2w5kE7EBpACggN+R3QgHYgNIEUEB4KA6IBJxAbgAIIDQUF0wARiA3AIwYEgITrgJGIDcBDBgaAhOuAEYgNwGMGBICI6kApiAzCA4EBQERxIBrEBGEJwIKjYcsAuYgMwiOBAkBEd6C5iAzCM4EDQER04GWIDSAOCA2FAdKArxAaQJgQHwoLowPcRG0AaERwIE6IDxxAbQJoRHAgbogPEBuACggNhRHCEF7EBuITgQBgd23IQHuFCbAAuIjgQZkRHeBAbgMsIDoQd0RF8xAbgAQQHwBFLkBEbgEcQHMB/ER3BQmwAHkJwAB0RHcFAbAAeQ3AAx+OIxd9Sio0HH3xQGRkZmjt3rkPjAJAIDuBEiA7/STo2GhsbtXTpUo0YMcLJeQD8L4IDODGiwz+Sio1Dhw6poqJCTz75pHr27On0TAD+F8EBnBxHLN6XVGxUV1dr2rRpmjx58kmfm0gkFI/HO1wAuo/gALqP6PCmbLsvWLlypbZs2aLGxsZuPb+mpkb33nuv7cEA/Fd+0z41j+7j9hiAb3w3OL4akuXiJJBsbjb27t2rOXPm6Pnnn1dubm63XrNgwQLFYrH2a+/evUkNCoQdGw4gOWw73Gdrs9HU1KQDBw5o1KhR7Y+1traqoaFBS5YsUSKRUFZWx4KMRCKKRCLOTAuE3LHgYMsB2Me2wz22YmPSpEnatm1bh8duuOEGlZeX68477zwuNACYwbEKkBrCI71sxUZ+fr6GDRvW4bEePXqoqKjouMcBmEVwAM44Fh5Ehzm2bxAF4B0cqwDOYdthTsqxsX79egfGAJAKthyAswgPZ/HZKEBA8NsqgBn8NkvqOEYBAoRjFcActh3JY7MBBBBbDsAs/kS6PcQGEFAEB5AeRMfJcYwCBBjHKkD6cMzSNWIDCAGiA0iv7286wh4fxAYQIkQH4I6wbz24ZwMIIe7nANwTxptL2WwAIcWWA3BfWDYexAYQckQH4A1Bvs+D2AAgiegAvCZIWw/u2QDQAfdzAN7j9/s82GwAOA5bDsC7/LjxIDYAdInoALzNL+FBbAA4KaID8D4v32BKbADoNqID8A8vbT24QRSAbdxECviL2zeYstkAkBS2HIA/ubHxIDYApIToAPwrXfd5EBsAHEF0AP5nautBbABwFNEBBIOT4cENogCM4CZSIDiKPmxVr4+Tv7mU2ABgTH7TPqIDALEBwDyiAwg3YgNA2hAdQDgRGwDSjugAwoXYAOAaggMIB2IDgKvYcgDBR2wA8ASiAwguYgOApxAdQPDYio3a2lqNGDFCBQUFKigo0Pjx47V27VpTswEIMYIDCA5bsdG3b189+OCDampq0ubNm3XppZdqxowZ+uCDD0zNByDE2HIAwWDrs1GmT5/e4ev7779ftbW12rRpk4YOHeroYABwDJ+3Avhb0h/E1traqj/+8Y9qaWnR+PHju3xeIpFQIpFo/zoejyf7lgBCjugA/Mn2DaLbtm3Tqaeeqkgkop///OdatWqVhgwZ0uXza2pqVFhY2H5Fo9GUBgYAjlcAf8mwLMuy84IjR45oz549isVievnll/XUU09pw4YNXQZHZ5uNaDSqydHZys6MpDY9AIhNB5AOR789rP95/W7FYjEVFBTYeq3tY5ScnBydddZZkqTRo0ersbFRjz32mJYuXdrp8yORiCIRogKAOd/dchAegPckfc/GMW1tbR02FwDgJsID8B5bsbFgwQJNnTpVpaWlam5u1ooVK7R+/XrV1dWZmg8AksYNpYA32IqNAwcO6LrrrtP+/ftVWFioESNGqK6uTj/60Y9MzQcAKWPbAbjLVmw8/fTTpuYAgLQgPID047NRAIQWv0ILpEfKN4gCgN+x7QDMIjYA4DsID8B5HKMAQBc4ZgGcwWYDAE6CbQeQGmIDAGwgPAD7OEYBgCRxzAJ0D5sNAEgR2w7gxIgNAHAQ4QEcj9gAAEMID+A/uGcDANKA+zsQZmw2ACCN2HYgjIgNAHAJ4YGw4BgFADyAYxYEGZsNAPAQth0IImIDADzq+5sO4gN+RWwAgE8QH/ArYgMAfIr4gF8QGwAQENzvAa8iNgAggNh6wEuIDQAIAeIDbiI2ACCEiA+kE7EBAOB+DxhFbAAAOmDrAacRGwCAEyI+kCpiAwBgC/EBu4gNAEBKuN8DJ0NsAAAcw9YDnSE2AADGEB+QiA0AQBoRH+GUaefJNTU1Gjt2rPLz89W7d29dccUV2r59u6nZAAABl9+077gLwWMrNjZs2KDq6mpt2rRJ9fX1+vbbbzVlyhS1tLSYmg8AEDIESPDYOkZ58803O3z9zDPPqHfv3mpqatKFF17o6GAAABzTWXBwBOMfKd2zEYvFJEm9evXq8jmJREKJRKL963g8nspbAgAgifs//CTp2Ghra9PcuXM1ceJEDRs2rMvn1dTU6N577032bQAA6Ba2H96VYVmWlcwLZ8+erbVr12rjxo3q27dvl8/rbLMRjUY1OTpb2ZmRZN4aAICkESDJOfrtYf3P63crFoupoKDA1muT2mzcfPPNWrNmjRoaGk4YGpIUiUQUiRAVAABv6OqGUyLEHFuxYVmWbrnlFq1atUrr169X//79Tc0FAEBacQxjjq3YqK6u1ooVK/Tqq68qPz9fX3zxhSSpsLBQeXl5RgYEAMAtBIgzbMVGbW2tJOniiy/u8PiyZct0/fXXOzUTAACexTGMfbaPUQAAwPGIkK7x2SgAABhEhBAbAAC44kR/hj1oIUJsAADgMUHbhhAbAAD4hF+3IcQGAAAB4OVtCLEBAECAeWEbQmwAABBS6dqGEBsAAKCDziLkaFuik2d2T2YqwwAAAJwMsQEAAIwiNgAAgFHEBgAAMIrYAAAARhEbAADAKGIDAAAYRWwAAACjiA0AAGAUsQEAAIwiNgAAgFHEBgAAMIrYAAAARhEbAADAKGIDAAAYRWwAAACjiA0AAGAUsQEAAIwiNgAAgFHEBgAAMIrYAAAARhEbAADAKGIDAAAYZTs2GhoaNH36dJWUlCgjI0OrV682MBYAAAgK27HR0tKikSNH6vHHHzcxDwAACJhsuy+YOnWqpk6d2u3nJxIJJRKJ9q/j8bjdtwQAAD5m/J6NmpoaFRYWtl/RaNT0WwIAAA8xHhsLFixQLBZrv/bu3Wv6LQEAgIfYPkaxKxKJKBKJmH4bAADgUfzqKwAAMIrYAAAARtk+Rjl06JB27tzZ/vWuXbu0detW9erVS6WlpY4OBwAA/M92bGzevFmXXHJJ+9fz5s2TJFVWVuqZZ55xbDAAABAMtmPj4osvlmVZJmYBAAABxD0bAADAKGIDAAAYRWwAAACjiA0AAGAUsQEAAIwiNgAAgFHEBgAAMIrYAAAARhEbAADAKGIDAAAYRWwAAACjiA0AAGAUsQEAAIwiNgAAgFHEBgAAMIrYAAAARhEbAADAKGIDAAAYRWwAAACjiA0AAGAUsQEAAIwiNgAAgFHEBgAAMIrYAAAARhEbAADAKGIDAAAYRWwAAACjiA0AAGAUsQEAAIwiNgAAgFFJxcbjjz+ufv36KTc3V+PGjdO7777r9FwAACAgbMfGiy++qHnz5mnhwoXasmWLRo4cqcsuu0wHDhwwMR8AAPC5DMuyLDsvGDdunMaOHaslS5ZIktra2hSNRnXLLbdo/vz5xz0/kUgokUi0fx2LxVRaWqqL+9yo7MycFMcHAADpcLTtiNbve1oHDx5UYWGhvRdbNiQSCSsrK8tatWpVh8evu+4668c//nGnr1m4cKEliYuLi4uLiysA16effmonHSzLsqxs2fDPf/5Tra2tOuOMMzo8fsYZZ+jjjz/u9DULFizQvHnz2r8+ePCgysrKtGfPHvtl5HHxeFzRaFR79+5VQUGB2+M4ip/Nn/jZ/ImfzZ+C/LNJ/z2Z6NWrl+3X2oqNZEQiEUUikeMeLywsDOQ/hiQVFBTws/kQP5s/8bP5Ez+bf2Vm2v/dEluv+MEPfqCsrCx9+eWXHR7/8ssvdeaZZ9p+cwAAEHy2YiMnJ0ejR4/WunXr2h9ra2vTunXrNH78eMeHAwAA/mf7GGXevHmqrKzUmDFjdN555+nRRx9VS0uLbrjhhm69PhKJaOHChZ0erfgdP5s/8bP5Ez+bP/Gz+VcqP5/tX32VpCVLlug3v/mNvvjiC/3whz/U7373O40bN872mwMAgOBLKjYAAAC6i89GAQAARhEbAADAKGIDAAAYRWwAAACj0hobQf1o+oaGBk2fPl0lJSXKyMjQ6tWr3R7JETU1NRo7dqzy8/PVu3dvXXHFFdq+fbvbYzmmtrZWI0aMaP9rf+PHj9fatWvdHstxDz74oDIyMjR37ly3R3HEPffco4yMjA5XeXm522M5Zt++fbr22mtVVFSkvLw8DR8+XJs3b3Z7rJT169fvuH+3jIwMVVdXuz1aylpbW3X33Xerf//+ysvL08CBA3XfffcpKL9/0dzcrLlz56qsrEx5eXmaMGGCGhsbbX2PtMVGkD+avqWlRSNHjtTjjz/u9iiO2rBhg6qrq7Vp0ybV19fr22+/1ZQpU9TS0uL2aI7o27evHnzwQTU1NWnz5s269NJLNWPGDH3wwQduj+aYxsZGLV26VCNGjHB7FEcNHTpU+/fvb782btzo9kiO+PrrrzVx4kSdcsopWrt2rT788EM9/PDD6tmzp9ujpayxsbHDv1l9fb0kaebMmS5PlrpFixaptrZWS5Ys0UcffaRFixbpoYce0uLFi90ezRE33XST6uvrtXz5cm3btk1TpkzR5MmTtW/fvu5/E9sf3Zak8847z6qurm7/urW11SopKbFqamrSNUJaSDruU3GD4sCBA5Yka8OGDW6PYkzPnj2tp556yu0xHNHc3GydffbZVn19vXXRRRdZc+bMcXskRyxcuNAaOXKk22MYceedd1rnn3++22OkxZw5c6yBAwdabW1tbo+SsmnTpllVVVUdHvvJT35iVVRUuDSRc7755hsrKyvLWrNmTYfHR40aZd11113d/j5p2WwcOXJETU1Nmjx5cvtjmZmZmjx5st555510jAAHxGIxSUrqE/+8rrW1VStXrlRLS0tg/vR+dXW1pk2b1uF/d0HxySefqKSkRAMGDFBFRYX27Nnj9kiOeO211zRmzBjNnDlTvXv31rnnnqsnn3zS7bEcd+TIET333HOqqqpSRkaG2+OkbMKECVq3bp127NghSXrvvfe0ceNGTZ061eXJUnf06FG1trYqNze3w+N5eXm2NorGP/VVSu6j6eEtbW1tmjt3riZOnKhhw4a5PY5jtm3bpvHjx+vw4cM69dRTtWrVKg0ZMsTtsVK2cuVKbdmyxfa5qh+MGzdOzzzzjAYPHqz9+/fr3nvv1QUXXKD3339f+fn5bo+Xks8++0y1tbWaN2+efvnLX6qxsVG33nqrcnJyVFlZ6fZ4jlm9erUOHjyo66+/3u1RHDF//nzF43GVl5crKytLra2tuv/++1VRUeH2aCnLz8/X+PHjdd999+mcc87RGWecoRdeeEHvvPOOzjrrrG5/n7TEBvyvurpa77//fmDOxo8ZPHiwtm7dqlgsppdfflmVlZXasGGDr4Nj7969mjNnjurr64/7r5Eg+O5/LY4YMULjxo1TWVmZXnrpJd14440uTpa6trY2jRkzRg888IAk6dxzz9X777+vP/zhD4GKjaefflpTp05VSUmJ26M44qWXXtLzzz+vFStWaOjQodq6davmzp2rkpKSQPy7LV++XFVVVerTp4+ysrI0atQoXXPNNWpqaur290hLbPDR9P528803a82aNWpoaFDfvn3dHsdROTk57XU+evRoNTY26rHHHtPSpUtdnix5TU1NOnDggEaNGtX+WGtrqxoaGrRkyRIlEgllZWW5OKGzTjvtNA0aNEg7d+50e5SUFRcXHxe655xzjv70pz+5NJHzdu/erbfeekuvvPKK26M45o477tD8+fN19dVXS5KGDx+u3bt3q6amJhCxMXDgQG3YsEEtLS2Kx+MqLi7WrFmzNGDAgG5/j7Tcs8FH0/uTZVm6+eabtWrVKv3lL39R//793R7JuLa2NiUSCbfHSMmkSZO0bds2bd26tf0aM2aMKioqtHXr1kCFhiQdOnRIn376qYqLi90eJWUTJ0487tfLd+zYobKyMpcmct6yZcvUu3dvTZs2ze1RHPPNN98oM7Pj/51mZWWpra3NpYnM6NGjh4qLi/X111+rrq5OM2bM6PZr03aMkupH03vZoUOHOvxX1a5du7R161b16tVLpaWlLk6Wmurqaq1YsUKvvvqq8vPz9cUXX0iSCgsLlZeX5/J0qVuwYIGmTp2q0tJSNTc3a8WKFVq/fr3q6urcHi0l+fn5x91X06NHDxUVFQXifpvbb79d06dPV1lZmf7xj39o4cKFysrK0jXXXOP2aCm77bbbNGHCBD3wwAO66qqr9O677+qJJ57QE0884fZojmhra9OyZctUWVmp7OzgnOJPnz5d999/v0pLSzV06FD97W9/0yOPPKKqqiq3R3NEXV2dLMvS4MGDtXPnTt1xxx0qLy+39//fzv6SzIktXrzYKi0ttXJycqzzzjvP2rRpUzrf3pi//vWvlqTjrsrKSrdHS0lnP5Mka9myZW6P5oiqqiqrrKzMysnJsU4//XRr0qRJ1p///Ge3xzIiSL/6OmvWLKu4uNjKycmx+vTpY82aNcvauXOn22M55vXXX7eGDRtmRSIRq7y83HriiSfcHskxdXV1liRr+/btbo/iqHg8bs2ZM8cqLS21cnNzrQEDBlh33XWXlUgk3B7NES+++KI1YMAAKycnxzrzzDOt6upq6+DBg7a+Bx8xDwAAjOKzUQAAgFHEBgAAMIrYAAAARhEbAADAKGIDAAAYRWwAAACjiA0AAGAUsQEAAIwiNgAAgFHEBgAAMIrYAAAARv1/iBgDnkCAjlMAAAAASUVORK5CYII=",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2593,7 +2603,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.contour.QuadContourSet at 0x7f1b2be23700>"
+       "<matplotlib.contour.QuadContourSet at 0x7a9f18553400>"
       ]
      },
      "execution_count": 53,
@@ -2602,7 +2612,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2625,7 +2635,411 @@
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(fit_intercept=False)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(fit_intercept=False)</pre></div></div></div></div></div>"
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(fit_intercept=False)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(fit_intercept=False)</pre></div> </div></div></div></div>"
       ],
       "text/plain": [
        "LogisticRegression(fit_intercept=False)"
@@ -2959,7 +3373,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.contour.QuadContourSet at 0x7f1b2bcb5c70>"
+       "<matplotlib.contour.QuadContourSet at 0x7a9f184e7610>"
       ]
      },
      "execution_count": 60,
@@ -2968,7 +3382,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2998,7 +3412,7 @@
     {
      "data": {
       "text/plain": [
-       "<Axes: xlabel='sepal length (cm)', ylabel='sepal width (cm)'>"
+       "<AxesSubplot:xlabel='sepal length (cm)', ylabel='sepal width (cm)'>"
       ]
      },
      "execution_count": 61,
@@ -3007,7 +3421,7 @@
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -3110,7 +3524,7 @@
     {
      "data": {
       "text/plain": [
-       "<Axes: xlabel='sepal length (cm)', ylabel='sepal width (cm)'>"
+       "<AxesSubplot:xlabel='sepal length (cm)', ylabel='sepal width (cm)'>"
       ]
      },
      "execution_count": 65,
@@ -3119,7 +3533,7 @@
     },
     {
      "data": {
-      "image/png": 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BaNK6Pjr2aI1Pl83XmPOIV4fio7iZmLtwNtp2jcarb75Uru+jIfYTkhEREZVDdk423p06HoePHlSNdYztjMXzl8PH28fi9+vTqx/mLPgQJ+KPoX3bDqo5HDpyEGtXrMephJN4/4N3MeN/sxDTsg2up6XigzlTAQDj3nhHdZ0Vq5fh3fH/w/TJMyGRSLBs5SJcTb6MtSs2oLpvdVxPS0VhUaHWOdy9ewcvvvoc2sS0w9drNqNatWpIOHMKMnlJYPHNpq+wfuM6fDRjLhpGNca2X7bgrQlj8Ou2XQjX8uDGu3fv4LVxozB4wBAs+HgRUlKuYsacqXB1ccXbb05UHffzzm14/tkX8d2GrZb6durEQIWIiCqFd6eOx7ETRzTGjp04gklT3saXn39j8fv5ePugc4cu2PnHdlWg8tfu31Hdtzratm6PV94cgddGvYHBA4YAAEKCQzFh7Lv4ZGmcRqDyf30H4JlBz6m+vnXnFhpGNUbTxs0AAMFPhOicw6YfvkG1al5YPH85nJ2dAQARYXVUr3/5zVqMefkN9OszAAAw+Z2pOBF/HF9v+gqzps0pc73NWzYiMLA2Zk79CCKRCJERdXE3Ix2fLpuPsa9PgFhckogJDw3H/yZONefbZjKmfoiIyOGlpCbj8NGDkMvlGuNyuRyHjx60Whqo/1ODsGvPH5BKiwAAO//4Bf369IdYLEZi0kWsXPMZots3Un188NEUZGSk4+HDh6prNGnUTOOazz87HL//tRMDn+uLhUvikHD2tM77X7x0ATHRrVVBirq8vAdIz7iLli1aaYy3bNEKV1OuaL3e1ZQriG7WEiKRSDXWqkUrFBTk487d26qxxg2b6vmuWBZXVIiIyOEpazJ0SU27pjXVUV5PdumOGQD2H9qHpo2b4VRCPKa+NxMAUFCQj7ffmIhe3fuUOc/V1VX1uYe7u8ZrXTp2w77fj+DA4X04cvwwXn79BQwf+hLenzS9zHXc3Nws+wcyknupOVsTV1SIiMjhhYaE6X09LCTcKvd1dXVDryd7Y+fvv+DXP3YgIrwOGjdsAgBoFNUEKanJCAsNL/OhTKHo4ufnj8EDhuDTeUsxbfJM/LDtO63HNagXhVNn4rXuFqpWzQsBNWuVWZFJOHsadevU03q9yIi6OPNPAgRBUI2dPnsanp7VEFirtt45WwsDFSIicngRYXXQMbYzJBKJxrhEIkHH2M5WWU1R6v/UIOw/9De2bd+C/k8NUo2PfX08tv/6E1asXorLV5JwNfkKfvtzB5as+FTv9ZZ9vhh79u1C6vVruHwlCfsP/o3IiEitxw4fNhJ5+XmYNOVt/PvfP7iWmoJffv0JydeuAgBeHfka1m5Yjd//2onka1fx6bL5SLx0AS+9MErr9V54bgTu3LmNOfNn4WrKFezZtwvLVy/BqBdfNRhcWQtTP0REVCksnr8ck6a8rbHrp33bDlg8f7lV79uuTSx8fHyRci0Z/fsOVI13iu2C1Z99iZVffIa1G1bDyckZdcLr4NnBw/Rez9nZGYuXL8TNWzfg5uqGVtGtsXjBCq3HVvetjq/XbMYnS+ZhxKtDIZZI0LBBI7RqEQMAeOmFUcjLe4D5i+bi3r0sRNapi8+XrdUZuNWqFYg1K9Zj4ZI4bHnuKfj6+GDIoOfw5pi3zfzulJ9IUF/fcTC5ubnw8fFBj4DRcBK72Ho6RERkhhrBPhg9rx8CagZCIir/v5+vpaYgNe0awkLCrbqSQvrJBRnSM+5g3bTfkHkjR+M1mUKKPenrkJOTA29vb73XsWnqRy6X44MPPkBERATc3d0RGRmJOXPmwIFjJyIisrHwsAh06diNQUolYdPUz4IFC7Bq1Sp8/fXXaNy4MU6dOoVRo0bBx8cH48ePt+XUiIiIyA7YNFA5evQoBg4ciH79+gEAwsPD8d133+HkyZO2nBYRERHZCZumfmJjY7F3714kJSUBAM6dO4fDhw+jb9++Wo8vKipCbm6uxgcRERFVXjZdUZkyZQpyc3MRFRUFiUQCuVyOuXPnYvjw4VqPj4uLw+zZsyt4lkREZE2CQmBtYiUlCAIERfneW5uuqGzZsgWbNm3C5s2bkZCQgK+//hqffvopvv76a63HT506FTk5OaqPtLS0Cp4xERFZWt79h5AVy6EQZIYPJoehEGSQFcvx4H5Bua5j0xWVyZMnY8qUKRg2rGRPedOmTZGamoq4uDiMHDmyzPGurq4abYeJiMjxFT0sxundl9BhoAuq+/pBbIEtymRbCkGG+9n3cHr3JUgfli8AtelPQ0FBQZlOdxKJBAqFwkYzIiIiWziw5RwAoFXPBnBylmg8FI8ciyAIkBXLcXr3JdX7Wh42DVT69++PuXPnIjQ0FI0bN8aZM2ewePFivPLKK7acFhERVTBBAPb/cA5Hd/wHr+oeEIkZqDgqQSHgwf2Ccq+kKNk0UFm+fDk++OADvPXWW0hPT0dQUBBef/11zJw505bTIiIiG5E+lCHrIXd00mNsoU9EREQVymFa6BMRERHpw0CFiIiI7BYDFSIiIrJbDFSIiIjIbjFQISIiIrvFQIWIiIjsFgMVIiIislsMVIiIiMhuMVAhIiIiu8VAhYiIiOwWAxUiIiKyWwxUiIiIyG4xUCEiIiK7xUCFiIiI7BYDFSIiIrJbDFSIiIjIbjFQISIiIrvFQIWIiIjsFgMVIiIislsMVIiIiMhuMVAhIiIiu8VAhYiIiOwWAxUiIiKyWwxUiIiIyG4xUCEiIiK7xUCFiIiI7BYDFSIiIrJbDFSIiIjIbjFQISIiIrvFQIWIiIjsFgMVIiIislsMVIiIiMhuMVAhIiIiu8VAhYiIiOwWAxUiIiKyWwxUiIiIyG4xUCEiIiK7xUCFiIiI7BYDFSIiIrJbDFSIiIjIbjFQISIiIrvFQIWIiIjsFgMVIiIislsMVIiIiMhuMVAhIiIiu8VAhYiIiOwWAxUiIiKyWwxUiIiIyG4xUCEiIiK7xUCFiIiI7BYDFSIiIrJbDFSIiIjIbjmZcvDFixfx/fff49ChQ0hNTUVBQQFq1qyJ6Oho9O7dG8888wxcXV2tNVciIiKqYoxaUUlISECPHj0QHR2Nw4cPo23btnjnnXcwZ84cvPjiixAEAdOnT0dQUBAWLFiAoqIia8+biIiIqgCjVlSeeeYZTJ48GVu3boWvr6/O444dO4Zly5Zh0aJFmDZtmqXmSERERFWUUYFKUlISnJ2dDR7Xvn17tG/fHsXFxeWeGBEREZFRqR9jgpTyHE9ERESkjUnFtErx8fHYt28f0tPToVAoNF5bvHixRSZGREREZHKgMm/ePMyYMQMNGjRArVq1IBKJVK+pf05ERERUXiYHKsuWLcNXX32Fl19+2QrTISIiInrM5IZvYrEYHTp0sMZciIiIiDSYHKhMnDgRK1eutMZciIiIiDSYnPp577330K9fP0RGRqJRo0Zldvj89NNPFpscERERVW0mByrjx4/Hvn370K1bN/j7+7OAloiIiKzG5EDl66+/xrZt29CvXz+LTODmzZt4//338ccff6CgoAB169bF+vXrERMTY5HrExERkeMyOVDx8/NDZGSkRW5+//59dOjQAd26dcMff/yBmjVr4vLly6hevbpFrk9ERESOzeRA5cMPP8SsWbOwfv16eHh4lOvmCxYsQEhICNavX68ai4iIKNc1iYiIqPIwOVD57LPPcPXqVdSqVQvh4eFlimkTEhKMvtaOHTvQu3dvPPvsszhw4ACeeOIJvPXWWxgzZozW44uKijSezJybm2vq9ImIiMiBmByoDBo0yGI3T05OxqpVqzBp0iRMmzYN8fHxGD9+PFxcXDBy5Mgyx8fFxWH27NkWuz8RERHZN5EgCIKtbu7i4oKYmBgcPXpUNTZ+/HjEx8fj2LFjZY7XtqISEhKCHgGj4SR2qZA5ExERUfnIFFLsSV+HnJwceHt76z3W5IZv8fHxOHHiRJnxEydO4NSpUyZdq3bt2mjUqJHGWMOGDXH9+nWtx7u6usLb21vjg4iIiCovkwOVsWPHIi0trcz4zZs3MXbsWJOu1aFDB1y6dEljLCkpCWFhYaZOi4iIiCohkwOVCxcuoGXLlmXGo6OjceHCBZOuNXHiRBw/fhzz5s3DlStXsHnzZqxZs8bkgIeIiIgcgzQqGJm9jd/ha3Kg4urqirt375YZv337NpycTKvNbd26NX7++Wd89913aNKkCebMmYOlS5di+PDhpk6LiIiI7Jw0Khg3nnSD55DbRp9jcjHt888/j9u3b2P79u3w8fEBAGRnZ2PQoEEICAjAli1bTJt1OeTm5sLHx4fFtERERHYuo18kMjsVIzw4A4tqf4uYxneNKqY1eXvyp59+is6dOyMsLAzR0dEAgLNnz6JWrVrYuHGjebMnIiIihyeNCtY6nhPpisxOxRgTcwhDvBOQ98D4a5ocqDzxxBP4559/sGnTJpw7dw7u7u4YNWoUnn/++TLN34iIiKhqUKZ1tKkdexODAxMxxNv4prBKJgcqAODp6YnXXnvNnFOJiIioknmc1rmJnoGJZV43J0BRMipQOX78ONq1a2fUBQsKCpCSkoLGjRubPSkiIiKyf8pVlKJQqSqtY2lG7foZMWIEevfujR9//BH5+flaj7lw4QKmTZuGyMhInD592qKTJCIiIvuiDFJqx960WpACGLmicuHCBaxatQozZszACy+8gPr16yMoKAhubm64f/8+EhMTkZeXh8GDB2PXrl1o2rSpVSZLREREtqee6vmi/mar3svk7cmnTp3C4cOHkZqaiocPH6JGjRqIjo5Gt27d4OfnZ615asXtyURERBXHUqmevAcK621PjomJQUxMjFkTIyIiIseknurpaeYOHnOYteuHiIiIKqeMfpFaxysq1VMaAxUiIiLSSOuEB2eUed3cPijlxUCFiIioisvoF4kHYaIKT+sYg4EKERFRFWCovX14cEaFp3WMwUCFiIioktPX3t6azdoswaxAZe/evdi7dy/S09OhUCg0Xvvqq68sMjEiIiIqP/W0jjb2luopzeRAZfbs2fjoo48QExOD2rVrQyQSWWNeREREVA7SqGC7T+sYw+RAZfXq1diwYQNGjBhhjfkQERFROVXEM3gqismBilQqRWxsrDXmQkREROVkzzt4zGHUQwnVjR49Gps3O+byERERUWUljQpWPYOndmxJYzZHD1IAI1dUJk2apPpcoVBgzZo12LNnD5o1awZnZ2eNYxcvXmzZGRIREZFelSnVU5pRgcqZM2c0vm7RogUA4Pz58xafEBEREWmnq719ZUr1lGZUoLJv3z5rz4OIiIh0UN/Bo40j7+oxxORi2ldeeQXLli2Dl5eXxnh+fj7efvtt9lEhIiKyoNJpHW0q2yqKOpEgCIIpJ0gkEty+fRsBAQEa45mZmQgMDIRMJrPoBPXJzc2Fj48PegSMhpPYpcLuS0REVBHUd/BUphWTvAcKxDS+i5ycHHh7e+s91ugVldzcXAiCAEEQ8ODBA7i5PW7FK5fL8fvvv5cJXoiIiMh0laVZmyUYHaj4+vpCJBJBJBKhfv36ZV4XiUSYPXu2RSdHRERU1VTmHTzmMDpQ2bdvHwRBwJNPPolt27bBz89P9ZqLiwvCwsIQFBRklUkSERFVBZU11VMeRgcqXbp0AQCkpKQgNDSUz/ghIiIygzQqWOu4MtXDVRRNRgUq//zzj8bX//77r85jmzVrVr4ZERERVVLKtI42TPVoZ1Sg0qJFC4hEIgiCYHAlRS6XW2RiRERElYl6Wkcbpnq0MypQSUlJUX1+5swZvPfee5g8eTLat28PADh27BgWLVqEhQsXWmeWREREDkp9Bw9XTExnVKASFham+vzZZ5/FZ599hqeeeko11qxZM4SEhOCDDz7AoEGDLD5JIiIiR6RM9dSOvYnBlbC9fUUwuTPtv//+i4iIiDLjERERuHDhgkUmRURE5Oi4g8cyxKae0LBhQ8TFxUEqlarGpFIp4uLi0LBhQ4tOjoiIyNFIo4KR0S8SmZ2K8dLTexmklJPJKyqrV69G//79ERwcrNrh888//0AkEmHnzp0WnyAREZGjYKrH8kwOVNq0aYPk5GRs2rQJiYmJAIChQ4fihRdegKenp8UnSEREZG8y+kVqHS9pec9UjyWZHKgAgKenJ1577TVLz4WIiMiuqe/g0Ya7eizPqEBlx44d6Nu3L5ydnbFjxw69xw4YMMAiEyMiIrInpdM62jBIsTyjApVBgwbhzp07CAgI0Lv9WCQSseEbERFVOsriWKZ1Kp5RgYpCodD6ORERUWXGJxnbnsk1KoWFhXBz0/6cAiIiospCPdXTkzt4bMbkQMXX1xdt2rRBly5d0LVrV8TGxsLd3d0acyMiIrIJpnrsh8mByp49e3Dw4EHs378fS5YsgUwmQ0xMjCpw6dmzpzXmSUREZFHSqGCt40z12BeRIAiCuSfLZDLEx8fjiy++wKZNm6BQKCq0mDY3Nxc+Pj7oETAaTmKXCrsvERE5NmV7e22Y6rG+vAcKxDS+i5ycHHh7e+s91qw+KklJSdi/f7/qo6ioCP/3f/+Hrl27mnM5IiKiCvM4rZOh9XWmeuyLyYHKE088gYcPH6Jr167o2rUr3n//fTRr1gwikfbIlIiIyB5wB49jMjlQqVmzJhITE3Hnzh3cuXMHd+/excOHD+Hh4WGN+REREZlEV3t75ZOMmdZxLCYHKmfPnkV2djYOHjyIAwcOYNq0abhw4QJatGiBbt26Ye7cudaYJxERkV6G2tuHB2cwreOAylVMm5WVhf3792P79u347rvvWExLREQ2UTqtow1XUeyHVYtpf/rpJ1UR7YULF+Dn54eOHTti0aJF6NKli9mTJiJyBP5B3vCr7Y17t3ORdSvX1tMhPN7Bw7RO5WRyoPLGG2+gc+fOeO2119ClSxc0bdrUGvMiIrIr7tVc8Ozkrqjf6nHvjaTTN7Bl4X4U5kttOLOqSz3Vw7RO5WVyoJKenm6NeRAR2bVnJ3dF3RZBGmN1WwThuf91xTezdtloVlUXd/BUHWb1USEiqkr8g7w1VlKUxBIx6rcKhn+QN9NAFYipnqqFgQoRkQF+tfUX+/nVZqBiabra2zPVU/UwUCEiMuDebf1BiKHXyTTKtI42TPVUPQxUiIgMyLqVi6TTN1C3RRDEErFqXCFX4MrZW1xNsSD1tI42XEWpehioEBEZYcvC/Xjuf5q7fq6cvYUtC/fbblKVCHfwkC5GBSpPP/200Rf86aefzJ4MEZGtGOqPUpgvxTezdrGPihVwBw/pY1Sg4uPjY+15EBHZhKn9UbJuMUCxJPVUD1dRSBujApX169dbex5ERDbB/ii2oZ7q4SoK6SM2fAgRUeWk7I+iXiALaPZHIctTpnoYpJAxzCqm3bp1K7Zs2YLr169DKtVcGk1I4A8cEdmGqfUj7I9iXRn9IrWOM9VDpjA5UPnss88wffp0vPzyy9i+fTtGjRqFq1evIj4+HmPHjrXGHImI9DL3OTzsj2Id6mkdbbiKQqYwOVD5/PPPsWbNGjz//PPYsGED/ve//6FOnTqYOXMm7t27Z405EhHpZW6dCfujWJ4yrVM79iYGByZqPYZBCpnC5EDl+vXriI2NBQC4u7vjwYMHAIARI0agXbt2WLFihWVnSESkR3mfw2OJ/ijcslyCO3jIGkwOVAIDA3Hv3j2EhYUhNDQUx48fR/PmzZGSkgJBEMyeyPz58zF16lRMmDABS5cuNfs6RFS1lLfOpDz9UcxNOVU23MFD1mTyrp8nn3wSO3bsAACMGjUKEydORM+ePTF06FAMHjzYrEnEx8fjiy++QLNmzcw6n4iqLkvVmWTdysXl0zdMWhHRl3KqKpSpnmrP32KQQlZh8orKmjVroFAoAABjx46Fv78/jh49igEDBuD11183eQJ5eXkYPnw41q5di48//ljvsUVFRSgqKlJ9nZtbdZdYiaiErepMyptyqgwy+kU+annPVA9Zj8mBilgshlj8+C+DYcOGYdiwYWZPYOzYsejXrx969OhhMFCJi4vD7Nmzzb4XEVVOptaZRLYIQkiDAKRdSsfVs7fMumdV2dosjSobjAFgqocqjFl9VO7fv48vv/wSFy9eBAA0atQIo0aNgp+fn0nX+f7775GQkID4+Hijjp86dSomTZqk+jo3NxchISEm3ZOIKh9j60yq16qGNxYPgKePm2osP6cQqybuQHZ6nkn3rApbm5VpHW2Uu3oYpJC1mRyoHDx4EAMGDIC3tzdiYmIAlPRW+eijj7Bz50507tzZqOukpaVhwoQJ2L17N9zctP+PUJqrqytcXV1NnTIRVRGGnsPzxuIB8PDW/DvEw9sVby4ZgLjhpqUuKvvWZvW0jjZM9VBFEQkmbtVp2rQp2rdvj1WrVkEikQAA5HI53nrrLRw9ehT//vuvUdf55ZdfMHjwYNU1lNcRiUQQi8UoKirSeE2b3Nxc+Pj4oEfAaDiJXUz5YxBRFRPZIgijPu6j8/X1M/40OQ3k5ulSJuXk6Lt++CRjqgh5DxSIaXwXOTk58PbWn0Y1eUXlypUr2Lp1q0YQIZFIMGnSJHzzzTdGX6d79+5lgppRo0YhKioK77//vsEghYiqtujudVGnWRCS/7mFM3uvGDw+pEGAwdeNCVRKp5fM3dpsj9SbtfV0kLSOc7IMLqkySMOdUByh+1easceR/TH53WrZsiUuXryIBg0aaIxfvHgRzZs3N/o6Xl5eaNKkicaYp6cn/P39y4wTESnVruOH1xf1h5NzyT9morvXxcBxHbBq4g7cvXZf53lpl9L1XtfQ6/p6phhKOTkCR9vBI85WoPbb2ah24PFO0Lwurri93BcKX7HJx5H9MjlQGT9+PCZMmIArV66gXbt2AIDjx49j5cqVmD9/Pv755x/VseyLQkSW9vqi/pA4af6CkTiJ8eaSAfhw8Nc6z7t69hbycwrh4e0KkUikGhcEAQW5RQZXU8xt02/vHDXVU/vtbHgeLtIY8zxchNpvZ+PmRj+TjyP7ZXKNivrWZK0XFIkgCAJEIhHkcnm5JmcIa1SIqpbo7nXxzETdBfvblhzUmwbyDaiGN5eYvuvHP8gbE9cM0fn6kte2OuSKinrLe0dJ9QAlaZw6XTN0vp58oCaKI5yMPo4qnlVrVFJSUsyeGBFRedRpFmTw9dKBSun6kbjhm02ub3H0nikZ/SK1jpekejIcItWjziVVpv/1azIURzgZfRzZN5PfobCwMGvMg4jIoOR/biG6e129rytpqym5evYmBAB1WzwBoGSFpmnnOgZ36ThqzxT1tI42jpTqUScN0/+rSxruZNJxZN/Mepc2btyI1atXIyUlBceOHUNYWBiWLl2KiIgIDBw40NJzJCICAJzZewWDJ3SCSIQydSaCAI3VEW01JXWal12RMabOxBF7ppRO62jjiEEKABTXcUJeF1d4Hi6CSK3CQJAA+R1dVaskxh5H9s3kkudVq1Zh0qRJeOqpp5Cdna2qQ/H19eVTj4kIQEm6pd6j591Y4lzlWGSLIIjFIo0gBcCj/ksi1TnK5/CoBxXK40qfq/5sHn22LNyPK6UKbvW16bcVaVSwagdP7diSHTxDvBO0flibc7IMnvsK4ZyiPwVjjtvLfZHfUbN5X37Hkt085hxH9svkcHL58uVYu3YtBg0ahPnz56vGY2Ji8N5771l0ckTkWPRt4TXUAM2YVI0hyloRQzUl+s7Vxdg2/bZkLzt4KmJLsMJXjJsb/eCcIoPLNd39UYw9juyXWcW00dHRZcZdXV2Rn59vkUkRkWMqzxZeY1M1+ihrRcypGTH2HHvtmWJPO3gqcktwcYRxgYexx5H9MTm0jYiIwNmzZ8uM//nnn2jYsKEl5kREDkhXusWY1IopqRptFHIFkk7fUAUQypoShVyhcVxJLYug91xHoyvVYyvOyTJUO6BZEwIAIjlQ7UCRVdJAVLmZHF5OmjQJY8eORWFhIQRBwMmTJ/Hdd98hLi4O69ats8YcicgBlGcLrzmpGnXKWhH1tMyWhfvLPIfn6rmSGhP1VJK2c+0xaJFGBWsdt4dUjzpuCSZLM/mnZfTo0XB3d8eMGTNQUFCAF154AUFBQVi2bBmGDRtmjTkSkQMozxbe8m7vfaJeDQyf0R0RTWurxpS1MZ4+bmUCEPWgpCC30O4fLKhM62hjD6keddwSTJZmcmdadQUFBcjLy0NAgP6HfVkLO9MS2ZeXZvfSuYXXUI2KtnOVfz2V3opszFh57mvsudYmjQpGTqSrqjGbNvbYrO2JEfd0bglm23oCrNyZ9uHDhxAEAR4eHvDw8EBGRgaWLl2KRo0aoVevXmZPmogcn7Z0i7FbeLWdq60+xdgx9doYXakcZW2MOedam73s4DHH7eW+ZXb9cEswmcvkQGXgwIF4+umn8cYbbyA7Oxtt2rSBi4sLMjMzsXjxYrz55pvWmCcROQDlFt7IFkEIaRCAtEvpeh/2V/o49e2/9Vo+gdiBjcs9J221Mcr7KhQKHWfpPrc88mXZKJDnwEPiA08nXwDaa098auQi2C0FgaFyzHzyDwAlRaouqY6xvZZbgsmSTP7JSUhIwJIlSwAAW7duRWBgIM6cOYNt27Zh5syZDFSIqjBj+6hUr1UNbyzW/nBA5fZfhVxhkUBFvf5F232NPbc8pIpC/JO9G5nSNNVYDZcQNGz9MgoiHzcjE9cswKdbv0Xn/ZdKBr4F8mNdIACodvTx98/SPUmshVuCyRJM/ikvKCiAl5cXAGDXrl14+umnIRaL0a5dO6Smplp8gkTkOPT1UVH3xuIB8PDW7Bbq4e2KN5cMUH199ewtKBRltxNr22Ksayw/p1BjRUTbfStiy/I/2buRJb2hMZYpvYFz17bgQZhI9bHw12/QITlJ4ziPo1J4HtUs6lX2JCGqCkwOVOrWrYtffvkFaWlp+Ouvv1R1Kenp6QYLYoio8jK2j0pkiyB4+rhpbYPv6eOGyEeBjr52+caOefq4GXXf0mOWbI2fL8tGpjQNAkrvWxCQe/MS8lxuoShUitah/6Lr+cuQKDSPEz360BhjTxKqQkxek5s5cyZeeOEFTJw4Ed27d0f79u0BlKyuaOtYS0RVg7F9VEIa6N8lGNIgAFfP3jJ4nCnzMua+R375D1fO3LRYHxVl7UluVj6Qqfs4WXoWnANrIOhWjt7rJQG4CqAugHqPxsrTk8TjQCHczxSjoJUzHnYyLhVGZAsm/4QPGTIEHTt2xO3bt9G8eXPVePfu3TF48GCLTo6IHIexfVTSLqXrPU75uqHjTJ2XoeslnUrTW/hrCuW2YgCQ1agN/Kv7WKcAfwDArSAfra/fA/ACgL/UxnoD+A7m9SRxSpUhbEAmnO4/XrmRVRchdWcNyEJZT0L2x6xKrMDAQERHR0Msfnx6mzZtEBUVZbGJEZFj0dW2vnS9x9Wzt5CfU6izpkQZLFw9ewuyYrnZNSrm3rc8pFHBqiBFWXdS3CwAHnUbAKJSf92KxPCo2wDOgTUAADeC/XC1Q00IEs3DXgCwp9R99gAY5gSzVlPCBmRCcl/zeyC5LyCsv55lHyIbsu+ScSKyK/5B3qin57k9Wxbux5VSv/CV9R6RLYLQdWgLRLYIwqqJO1CQq/nQuoLcIqyauEPjXk7OErNrVLTVmRhzX3MpA5QbT7rhQZgI0sx05CddhDQrA7WfHQGPyHoax3tE1kPtZ0dojP2yMBr5HR8X+yahZCWl1GNzIAewSwacWpGLk0sf4MahQtVrzskyeO4r1Fq/4nGgEE73hbI1LwCc7gvw3pJf5lxt10tJluHgvkJcM1Ajo28uRMbiOh8RGWTstmNtzw90dpFg4pohWrci+wd56+y3Ys7zf35adhgP7hXorDPJTs9D3PDNRvd5MZb6Koq8IB+3t36LgiuXVK971G2A2s+OgLwgH8VZmXD2rwEX/5oAABkef/8KfZw1+o/E7y8E1j/Ued8XFz5+Yn0PXxG+qu+EkJPFqrHS25jdzxSXuYa62u89/p5p2xZ9PdYFLwI4pDbWsYsrFi33hY/aVmlxtqJMwzdH2VJN9oc/MURkkLHbjrUdF94kUOdW5Ktnb2H/D2e1Bgvm9DBJ/e8OLhuxrVjffU1ROtUDoCRIuXpZ47iCq5dx+8eNcPGvCc/6DVVBii7FEU7I7+aGwCeNL3Ldly1gzEnNQKT0NuaH0c5GX0/btujXjkpxtNTYscNFeLfUVunab2fD87DmyhW3VJO5GKgQkV7GbjvWdZy+rcORpYIac1m674kxSqd6AECamV6ykiKU6ngrKFBw5RKkWdqf16NLcBc39KgugsTwoZCjJE2kHiKV3sZc0MUNsuqiMhultSm9LVpnGkoOHD5QpEoDOSfLUO2A5nN+tM2FyFgMVIhIL2O2HRtznDYNYkLK1Lwoa1kadwg3+jqW7HtijNKrKErF97L0nlecZXrBatzOGuhWXfuTk7W5omXM5drj4CB1Zw3Iq4vwF4CPAOw2cL0kAH8AOGjguOuP7uGSqj8QUZ8LkTFYo0JEehm77dicVE3soMaIHVTSJj/l31sICPODp7dx6Y71M/6EWCK2WN+T0rQ9gweAattx6SAFAJz9/PVe09m/hsnz8Ap1wopzgbhxqBC3ThdD7CRo1KaUVlfLmPo25pQcBdo8EHBP7XV/APEAItTGtG2L1if00T2kYfp/rZizpZqqNv7EEJFeym3HdVsEaaR1FHIFrpy9pQoSsm7lIj+nEB7erhqpHuV2YENj4U1ql7m3tuOU97VUzxNt1PuglKYtQFFyqVGyFbng6mXN9I9IDI/IegZrU/QJ7uSG4EeN2XotzsdemWYaRgKgBx43gwMAQVLy1GL1bcxDB2bhXqlFjSwAraHZl07btmgRUCZtJJEA7Tu6IvzRPYrrOCGviys8D2umf7TNhcgYTP0QkUH6th0r+Qd5G92ivjxj1k7zlO6DUvrDEGO3IpvLOVmG72UlQYm6HihpAqcuv2PJThul49/nlwlSlLLwOA2kqx5FW21L+44lu37U3V7uq7HNWttciIzF0JaItPIP8oZfbW9VauWbWbv0bus1VKOy6+tTEIvFUCgU6DUyxqS5JOy5jHu3H1hsO3F2qBsePsyCu7s/PDwep2NKp3XyLyei8MZ1uIWEwbNuAwAlBbPF97I0thirk7h7IPil11Ht9Akg5QqEOvWQ37INACA8Mx2h97KQ6l8Dqf41URQqRbM7J+Fy8Q6adJBhSP20MtcrzSVVhuoA/kRJ4ewVaLbVvz/EFS43Fcjr4oLst0rek5RkGdJSZTi+u0jrNZX2dHBCdFs3nDteCBzVXUuyuKcrqr3ogdBwJ9VKinOyDC6pMkjDS56YfHOjHzwOFsI9wXJt+kvfQ9cYVS58V4lIg7aeKVfO3gQA1G3xhGqsdB8VQzUq/x25hqxbufAP8jY5UGnZ4/EKhbb+LcaSKgpxtvgQ7p18vDfG+4kGiOj2IpxcPR7v3snKwPU1y6B4WKA6TuTmDtdatVGYmqwaU/ZHkbh7qMY8szLgvGYZTirP/ec02v61HR/WDESf6ymq4/6oH46Pbt/GzgclwcO2r4Ej1UWI21kDXnpa2avXgNSDZqoHAKpvLbme57FiSD7JxzPNnXEwQX//FKUeR2SocSQPzQ0c17KPKwK7lQQeWnumxLpABGhsby5PH5WKuAfZL76bRKRBWy+UyOZBiGyuv4+KsS30s27llqs1vrb+LcY6W3wI97Kvaozl3rqMy8e/1UjrlA5SAEAofKgRpACP+6Ooc16zDKdLnXvq4UMsUQtSAGBZ0jXEP9Bc4dh3X8BUA63slTUgpVvtCyibmnlRDhwxMkhxBtDz0ef1UfI8odLboiUAejkBgc96qsa09kw5KoVHqX4r5emjUhH3IPvFQIWIVEzphVK6jwpgXC1LZIugcrXG13ZfQ6RRwUjr6I179y8b7HGSfzmxTJCiU6lza15OxMmHBdpb3j/6+OPRf3W1xt9zX8DxLfllWtSrt6NX1oAotw5fhvF9T3QphmYPlu9Qtg6muxMQt+PxziadPVNKzQV43EfF/WChSW31zbkHe7VULkz9EJGKOb1Q/Gp7q1ZLCvOl+GbWrjL1LepCGgRYbK6lr61vS3F2keEeJy7+NVF447rJc1GeW83Aub2NvN7Laq3sO8W6YBOAkFKt7J8CcKjUtb8DUP3R15rrRsa5gsepJGUdzInR7kjJElC7o4vGSgpguGeKNqEv3ld9bkyqxpx7uFyTsV6lEuE7SUQq5vRC0XZO1i3dvU3SLqWbfA9D91UGKPq2FDtnGtfjxC041OS5KM/NM+NcQ44elWIMSoIGpdeOSnG01HF7ADyvdlykGffS1oOl5ohq8NXxS99QzxRDlKmamxv9dB5jzj3Yq6VyYeqHiFR01ZlY0tWzt5CfU2h2jUrpmhfldmJDW4qVPU4gKvXXnkgMj7oNVDt4nKvr/qVpSEa9KLRx9zCq5b2xSrfG1/dE5dIt9I0lQcmKTOkeLHld9Pc9MaVeRhtjUjWm3MOYOZPjYaBCRBq01ZnoY066aNXEHSjI1SyOLHhQhJTztzXGrp67havndNe8lO55YogxPU4MtcHXpnrKFXROuoiwrAxIX5+IVmq7gCxF2RrfUEpnP4xrea+um68Ia9s4a9S8GNv3RGvPlFgXFMS6GH1/j+NFeutWjL0He7VUTiKh9D9XHEhubi58fHzQI2A0nMTG/09BRIYp60wUcgVGfdxH53FLXttqdgt7bX1ZSte3SKOCUaOGB/xreCArswCZmY8LXbU9b8cY0qwMFGdlau2FIs1Mx7XP5pt0vSQ8Xo04WLcB3n12BFxupsErLRXpPj4488sPJs9R1z2SADQo99WADYu8Ib6pQFArZ1Rr6oL33s7GYbXtvx27lDRy8zFyq69zigwu10r1OHk0ppBo1qboo69uRf0e8urisluWuT3ZYeQ9UCCm8V3k5OTA21v/P3YYqBCRQS/N7qWzhf43s3ZZ7b76WtkD+tvZl4dv3Ayc1rJ7RxtnAOqbZGUiMY5G1sOYl15Xjd345ouybfWNpGyNr16j0gclNSnq89PW3l7r9R61vF+nVhcyesQ9HDtcBLlc/3Hl8cSIe2Xb6j/6r/q7qGy1r69uRef1jDyXbM+UQIVhJxEZZMy2Y0sz1MreWkFKeGY6/nxYUGZrri7F0HwCsZOgQOcrlxD2aMsyoD3lZCxtrfG1bR029l+cpVvepyTLcPiAZpACAHI5cPhAkcYW6fLQlr4xd4uxzi3L3J5cKbHiiIgMMmbbsaWo7+AxNRgx1N7eGKH3slRbc3cBOI6SupBv9JyzEcAxAO3xuGlaWFYmUh/NQdlWPzvnPIqS0+CadwPpuy/qvN54lKyaqLfGV1e6hf5NAGP0zG/OAm/UCpRotLxXSjOw/ff6NVmZc8yh8BXj5kY/VfpGckeO2u/r/hnSt8XY0JZlbk+uXPhOEpHR9G07NpW+nieAaWkdeUE+bm/9FgVXLqnGtLW3N8Z1P3/cQ8nTg/8y8hz13rT+AOIBpPo/foaQtvnp8394HPDoo2yhn2TguNbtXHUGGyEGtv+GWnirb3FESQ2Lc7L+YEPfFmNDW5a5Pbly4btJRBVOPa1jCbe3fltSA6JG2d4+WK1WxBjXagSgr7tHmTb4xsoC0BqAv9qKjrb56eIPzSBFax1HqTFly/vSdSvKOhN9KyIRdZzQsYurzhoVS6ymaKPcdqyrzsSYbdHmnEuOhzUqRFRhpFHBFg9SpJnpJSsVBlrjm3I9bW3wTZEFIOzgXv3z08LXV4R9bZw1xrTWcWgZ01a3UroeRZdFy33RvlT9iLHnlofWbcfl2RbN7cmVEsNOIqoQ6jt4LFkIa6jvibK9vSHK+pbiBzl6j/Nt3wny/AKE3byO83qCIMmVS0Dn7gbnN+lVd4juCWja0QXtHrWoT35Ux+F0R45APXUc6pR1K4cWeCNRRz2KLj6+Yqzb6IdrKTJcvyYz6dzyKF23or612dhz3Q8WwiOhGAWtnPGwk5uVZ0y2wECFiKzO0qso6pz9jGuNr4up9SPZx0qesHPewHHyug2Mmt8bXz4sKZj9uRB5O4pwe7mv0XUc2jzRzhUBZgYZ4REVE6CUpvzzmkKcrWAflSqC7yYRWY01Uj2lGdsaXxdT6keM5QQgtXN3vfPT1rZe+ewbJbaP163229nwPKzZ3bj0948qBwYqRGQVpZ/BY6zwzHRVO3q9189MR37SRUizMoxqja/rGsbWj5hCBmjUxmibn7b+KNr6gChrMdTb2xfEuiC/nO3jnZNletvW2zP2UalaqkboTURWo2+bsSkBik9BPj7d+i06q6VglO3oc9W2GOvbiiwvyNfZGl8bc57rYyz12hiJuwdqzhiFACSh4cN/MTr3EjoZ2UMkO0eBt/6RYo/a6z0uFiNuZw34yWFybUdlSJmwj0rVwneSiMyi3phNG1NTPZ9u/RaxpVIwsVcvY9GPGzXa0RvaimxKozdD9SPloayNKQqVIjy4ZHWlZ+BdDPG+Aedk3Y8FADT7gEwbkIl99zUTPfvuC5jaPxMrzgWa/AtZX8rEUVrPs49K1cJ3k4hMZum6k/DMdI2VFCX1dvSp/jUfp2pKU9uKbG5HWnOVecaOSAyPyHpw8a+pClJ6BiYCAIZ4JwAwvg/IjQOF2HO/bHN8OYA99wXcOFSIYBN2uihTJmX+DGopE0dYiWAflarFMdb5iMhuWKM4NlQtBaNei6EUlpUJwLityKYob+rnSQDdSo0pa2OKQqUa48ogRcmYPiC3zhTrvf+t0/pfL82YlImjYB+VqoNhJxGVoavuBDDvGTyG6Gpb3xslBafKdvTl3Ypc5ngzUj+7UFIsq/4cng4vvY5UhUKjNkYGqY4rlDCmh0hQtLOOsx+93kr/66VVppRJeXqwkGPhu0pEGtQbs5VmrS3GutrW7wHQx90D2Y9++Su3+hZcvay5U0ct3WIKlxoBgFgMKAzv+hGjZKeOent7mUiMo5H1kFm3ATxNuvNj+nqIBHdxQ4/qIuy7L2i2xgfQrbrIpLQPUDlTJub0YCHHwtQPEamop3W0fVjtvjra1ssBnHxYYHCrrzFbkbXJv5xoVJACANUi6mJ8RF2NsaOR9fCuGfc1RdzOGuhWXfN73626CHE7TVs9UmLKhBwNw1Ai0tjBY2pAEujpjloebrib/xB3CgrNur+hWpGClCsa246DX3od0qwMk7Yia1N447re133adUK1ulGqe0wCsCwrA2FZmUj1r4HUCijc9Qp1wopzgTi+JR//HpFqtNo3B1Mm5Gj400lUhVgyrePp7IRxzaPQPODxltZz6few4mwi8mWmFWUaqhVJ3/Gj6nNlzxQX/5rl3uHjFhyq9/Vq9RvB81ErfKVU/5oVEqAoZWcr8N7b2Tis3K3zcyE67ijCouW+8ClH3xOmTMhRMPVDVEVYOq0zrnkUmtSorjHWpEZ1jGsRZfK1dLbB10LZM8USPOtFldSoaCMWlwlSbOG9t7NxrFTfk2OHi/AuW8VTFcFAhaiSk0YFI6NfpEV36wR6uqN5gB8kYs3rScQiNA/wQ6CH6U+x1VZ7opVaz5Tykmam665RUSgsco/ySEmW4fCBIshLFe/I5cDhA0W4xlbxVAVw3Y+okrBUK3tj1DIQiNTydDe5XkXi7qFRe1L8IAfp27foPF69Rb25jOnLYskGcjcOFOLWmWIEtXLWu2MnJVmGtFQZ7t4pXV6s6fo1mU2edkxUkfgTTlQJVPSW4rsGgpC7+Q/Nvray9kSama73OFN7pmi9hoX7suiiSMvHuBfuaHSZ7fFo545X6OO/hsvUoxgQ6kB9T4jMxdQPkQMrndapqC3Fd/If4lz6PcgVmu3d5QoB59Lvmb37R53OuhWRGB51G1hkpaMi7gEAu58/ovN5Peq01aNoI5EAHbu4cjWFqgQGKkQOqnRxbEVbcTYR5zPva4ydz7yPFWcTLXYPS/ZMsdU9vE+l4FB2sdYeMcrn9QC661G0ad/RFYvY94SqCIbjRA6mPD1PLClfJsOCU+cR6OGGWp7u5eqjokvpupXy9Eyx1T1cLt7R+/qt08UI7uSGNAPP4ZmzwBu1AiUIDXfiSgpVKfxpJ3Ig6rUotgxS1N0pKLR4gFKaJXqm2Ooe0oaBel9XPq8nxMBzeFq3Y6qHqiamfogchK1TPWSe3JgIdPJ1hqTUuAQlBbXK3T8RdZzQsYsrJKUOZD0KVXU2DVTi4uLQunVreHl5ISAgAIMGDcKlS5dsOSUiuyONCtYIUqoiaWY68pMu2ryvibGKQkuenBwenIGegYlY/KuvUc/rWbTcF+1LPYeH9ShU1YkEQRAMH2Ydffr0wbBhw9C6dWvIZDJMmzYN58+fx4ULF+DpafhZFrm5ufDx8UGPgNFwErtUwIyJrEdfHxTAflI9FUlekI/bW79FwZXH/4BRttCXuHvYcGa6FYVKER5cElD1DEzEEO8E1Ws3DhXi1mnDfVSupchw/ZqM9ShUaeU9UCCm8V3k5OTA29tb77E2DVRKy8jIQEBAAA4cOIDOnTsbPJ6BClUWFd0HxVHc+OYLFFy9DAhq3WNFYnhE1kPwS6/bbmI6KIOUnoElO5/UgxQiesyUQMWuQvWcnBwAgJ+fn9bXi4qKUFT0uMdAbm5uhcyLyFrsZQePPZJmpmuspKiotdC3doGtsUqnehigEFmO3QQqCoUC77zzDjp06IAmTZpoPSYuLg6zZ8+u4JkRlR/TOqar6Pb25aVM9xCRZdlNoDJ27FicP38ehw8f1nnM1KlTMWnSJNXXubm5CAkJqYjpEZmNaR3zVFR7eyKyb3YRqIwbNw6//vorDh48iOBg7f/yBABXV1e4umr/C5/I3jCtUz7K9va6alTsaTWFiKzHptuTBUHAuHHj8PPPP+Pvv/9GRESELadDZDHKVRQGKeVTES30ici+2XRFZezYsdi8eTO2b98OLy8v3LlT0mrax8cH7u7utpwakVH01Z4wQCm/imihT0T2zaaByqpVqwAAXbt21Rhfv349Xn755YqfEJGR1NM62jBIsayKaKFPRPbJpoGKHbVwITKaPT5vh4iosrKLYloiR1HVW9kTEVU0BipEpeiqOwFYe0JEVNEYqBCpYc8TIiL7wkCF6BGmdYiI7A8DFary2JiNiMh+MVChKoM9T4iIHA8DFaoSWHtCROSYGKhQpca0DhGRY2OgQpUWi2OJiByfTR9KSGQtDFKIiCoHrqhQpcL29kRElQsDFao0uIpCttYzMNHWUyCqdBiokMPhNmOyJ0WhUoQHZ6BnYCKGeCfYejpElQ4DFXIY+rYYA0z1UMUqCpUCAIMUIitjoEIOgWkdsifqqygAGKQQWRF3/ZDdY5BC9kQZpCgxSCGyLq6okF3QV3cCMK1DRFRVMVAhm2N7eyIi0oWBCtkM29sTEZEhDFTIJtiYjYiIjMFAhayKPU+IiKg8GKiQVTCtQ0RElsBAhSyOaR0iIrIUBipkNqZ1iIjI2hiokMnU0zraMEghIiJLYaBCJmFah4iIKhIDFTIaW9kTEVFFY6BCGnTVnQCsPSEioorHQIVU9LWyB5jqISKiisdAhQAwrUNERPaJgUoVx8ZsRObpGZiIId4Jtp4GUaXHQKUK4w4eIuMVhUoRHpwBgEEKUUVioFJFMdVDZDxlkNIzMBEAGKQQVSAGKlUMUz1ExisKlQKAKkhhgEJU8RioVCFcRSEynTLdQ0S2wUClEtK3zZhBChERORIGKpUI0zpERFTZMFCpJJjWISKiyoiBioPR1eKeQQoREVVGDFQcBNvbExFRVcRAxQEwrUNERFWV2NYTIP0YpBARUVXGFRU7oK/uBGBah4iIqi4GKjbGnidERES6MVCxIaZ1iIiI9GOgYgN8ajEREZFxGKhYEXueEBERlQ8DFStQb2WvDYMUIiIi4zBQsTCmdYiIiCyHgYqZmNYhqtyKQqUID84AAPQMTMQQ7wQbz4ioamKgYiKmdYgqP2WQwgCFyPYYqJiAaR2iyq0oVAoADFKI7AgDFSOx5wlR5cZUD5F9YqBigHqqh0EKERFRxWKgogdTPURERLbFQEUHpnqIiIhsj4FKKUz1EBER2Y8qG6jo64MCMNVDRERkD6pkoKJee1IaAxQiIiL7UaUCFaZ1iIiIHEuVCVS4g4eIiMjxVLpAhc/gISIiqjwqTaDCZ/AQERFVPpUiUJHWD0I+V0yIiIgqHbGtJwAAK1euRHh4ONzc3NC2bVucPHnSpPNzIxikEBERVUY2D1R++OEHTJo0CbNmzUJCQgKaN2+O3r17Iz093ehrPAhhkEJERFQZ2TxQWbx4McaMGYNRo0ahUaNGWL16NTw8PPDVV1/ZempERERkYzatUZFKpTh9+jSmTp2qGhOLxejRoweOHTtW5viioiIUFRWpvs7JyQEAKIoKrT9ZIqrUFA+lkOWX/P1SmFeMPJHCxjMiqrzy8kr+/xIEweCxNg1UMjMzIZfLUatWLY3xWrVqITExsczxcXFxmD17dpnxlEUfWW2ORFR1pD367xEAH9tyIkRVxIMHD+Dj46P3GIfa9TN16lRMmjRJ9XV2djbCwsJw/fp1g39Qsr7c3FyEhIQgLS0N3t7etp4Oge+JveH7YV/4ftiOIAh48OABgoKCDB5r00ClRo0akEgkuHv3rsb43bt3ERgYWOZ4V1dXuLqW7ZPi4+PDHzI74u3tzffDzvA9sS98P+wL3w/bMHaBwabFtC4uLmjVqhX27t2rGlMoFNi7dy/at29vw5kRERGRPbB56mfSpEkYOXIkYmJi0KZNGyxduhT5+fkYNWqUradGRERENmbzQGXo0KHIyMjAzJkzcefOHbRo0QJ//vlnmQJbbVxdXTFr1iyt6SCqeHw/7A/fE/vC98O+8P1wDCLBmL1BRERERDZg84ZvRERERLowUCEiIiK7xUCFiIiI7BYDFSIiIrJbDh2orFy5EuHh4XBzc0Pbtm1x8uRJW0+pSoqLi0Pr1q3h5eWFgIAADBo0CJcuXbL1tOiR+fPnQyQS4Z133rH1VKqsmzdv4sUXX4S/vz/c3d3RtGlTnDp1ytbTqrLkcjk++OADREREwN3dHZGRkZgzZ45Rz52hiuewgcoPP/yASZMmYdasWUhISEDz5s3Ru3dvpKen23pqVc6BAwcwduxYHD9+HLt370ZxcTF69eqF/Px8W0+tyouPj8cXX3yBZs2a2XoqVdb9+/fRoUMHODs7448//sCFCxewaNEiVK9e3dZTq7IWLFiAVatWYcWKFbh48SIWLFiAhQsXYvny5baeGmnhsNuT27Zti9atW2PFihUASjrahoSE4O2338aUKVNsPLuqLSMjAwEBAThw4AA6d+5s6+lUWXl5eWjZsiU+//xzfPzxx2jRogWWLl1q62lVOVOmTMGRI0dw6NAhW0+FHvm///s/1KpVC19++aVq7JlnnoG7uzu+/fZbG86MtHHIFRWpVIrTp0+jR48eqjGxWIwePXrg2LFjNpwZAUBOTg4AwM/Pz8YzqdrGjh2Lfv36afx/QhVvx44diImJwbPPPouAgABER0dj7dq1tp5WlRYbG4u9e/ciKSkJAHDu3DkcPnwYffv2tfHMSBubd6Y1R2ZmJuRyeZnutbVq1UJiYqKNZkVAycrWO++8gw4dOqBJkya2nk6V9f333yMhIQHx8fG2nkqVl5ycjFWrVmHSpEmYNm0a4uPjMX78eLi4uGDkyJG2nl6VNGXKFOTm5iIqKgoSiQRyuRxz587F8OHDbT010sIhAxWyX2PHjsX58+dx+PBhW0+lykpLS8OECROwe/duuLm52Xo6VZ5CoUBMTAzmzZsHAIiOjsb58+exevVqBio2smXLFmzatAmbN29G48aNcfbsWbzzzjsICgrie2KHHDJQqVGjBiQSCe7evasxfvfuXQQGBtpoVjRu3Dj8+uuvOHjwIIKDg209nSrr9OnTSE9PR8uWLVVjcrkcBw8exIoVK1BUVASJRGLDGVYttWvXRqNGjTTGGjZsiG3bttloRjR58mRMmTIFw4YNAwA0bdoUqampiIuLY6BihxyyRsXFxQWtWrXC3r17VWMKhQJ79+5F+/btbTizqkkQBIwbNw4///wz/v77b0RERNh6SlVa9+7d8e+//+Ls2bOqj5iYGAwfPhxnz55lkFLBOnToUGa7flJSEsLCwmw0IyooKIBYrPnrTyKRQKFQ2GhGpI9DrqgAwKRJkzBy5EjExMSgTZs2WLp0KfLz8zFq1ChbT63KGTt2LDZv3ozt27fDy8sLd+7cAQD4+PjA3d3dxrOrery8vMrUB3l6esLf3591QzYwceJExMbGYt68eXjuuedw8uRJrFmzBmvWrLH11Kqs/v37Y+7cuQgNDUXjxo1x5swZLF68GK+88oqtp0baCA5s+fLlQmhoqODi4iK0adNGOH78uK2nVCUB0Pqxfv16W0+NHunSpYswYcIEW0+jytq5c6fQpEkTwdXVVYiKihLWrFlj6ylVabm5ucKECROE0NBQwc3NTahTp44wffp0oaioyNZTIy0cto8KERERVX4OWaNCREREVQMDFSIiIrJbDFSIiIjIbjFQISIiIrvFQIWIiIjsFgMVIiIislsMVIiIiMhuMVAhIiIiu8VAhYhM8vLLL2PQoEE6X9+wYQN8fX0rbD6GhIeHY+nSpSafl5WVhYCAAFy7ds3ic1LKzMxEQEAAbty4YbV7EDk6BipEVClYOkCaO3cuBg4ciPDwcItds7QaNWrgpZdewqxZs6x2DyJHx0CFiKiUgoICfPnll3j11Vetfq9Ro0Zh06ZNuHfvntXvReSIGKgQOZCtW7eiadOmcHd3h7+/P3r06IH8/HzV6+vWrUPDhg3h5uaGqKgofP7556rXrl27BpFIhO+//x6xsbFwc3NDkyZNcODAAdUxcrkcr776KiIiIuDu7o4GDRpg2bJl5Z739u3b0bJlS7i5uaFOnTqYPXs2ZDKZ6nWRSIR169Zh8ODB8PDwQL169bBjxw6Na+zYsQP16tWDm5sbunXrhq+//hoikQjZ2dnYv38/Ro0ahZycHIhEIohEInz44YeqcwsKCvDKK6/Ay8sLoaGhBp9c/Pvvv8PV1RXt2rXTGP/vv//wf//3f/D29oaXlxc6deqEq1evAnicEps3bx5q1aoFX19ffPTRR5DJZJg8eTL8/PwQHByM9evXa1yzcePGCAoKws8//2zOt5ao8rP1UxGJyDi3bt0SnJychMWLFwspKSnCP//8I6xcuVJ48OCBIAiC8O233wq1a9cWtm3bJiQnJwvbtm0T/Pz8hA0bNgiCIAgpKSkCACE4OFjYunWrcOHCBWH06NGCl5eXkJmZKQiCIEilUmHmzJlCfHy8kJycLHz77beCh4eH8MMPP6jmMXLkSGHgwIE657l+/XrBx8dH9fXBgwcFb29vYcOGDcLVq1eFXbt2CeHh4cKHH36oOkY5r82bNwuXL18Wxo8fL1SrVk3IysoSBEEQkpOTBWdnZ+G9994TEhMThe+++0544oknBADC/fv3haKiImHp0qWCt7e3cPv2beH27duq70tYWJjg5+cnrFy5Urh8+bIQFxcniMViITExUeefYfz48UKfPn00xm7cuCH4+fkJTz/9tBAfHy9cunRJ+Oqrr1TXGTlypODl5SWMHTtWSExMFL788ksBgNC7d29h7ty5QlJSkjBnzhzB2dlZSEtL07j20KFDhZEjR+qcD1FVxkCFyEGcPn1aACBcu3ZN6+uRkZHC5s2bNcbmzJkjtG/fXhCEx4HK/PnzVa8XFxcLwcHBwoIFC3Ted+zYscIzzzyj+trUQKV79+7CvHnzNI7ZuHGjULt2bdXXAIQZM2aovs7LyxMACH/88YcgCILw/vvvC02aNNG4xvTp01WBirb7KoWFhQkvvvii6muFQiEEBAQIq1at0vlnGDhwoPDKK69ojE2dOlWIiIgQpFKp1nNGjhwphIWFCXK5XDXWoEEDoVOnTqqvZTKZ4OnpKXz33Xca506cOFHo2rWrzvkQVWVONlvKISKTNG/eHN27d0fTpk3Ru3dv9OrVC0OGDEH16tWRn5+Pq1ev4tVXX8WYMWNU58hkMvj4+Ghcp3379qrPnZycEBMTg4sXL6rGVq5cia+++grXr1/Hw4cPIZVK0aJFC7Pnfe7cORw5cgRz585VjcnlchQWFqKgoAAeHh4AgGbNmqle9/T0hLe3N9LT0wEAly5dQuvWrTWu26ZNG6PnoH5tkUiEwMBA1bW1efjwIdzc3DTGzp49i06dOsHZ2VnneY0bN4ZY/DijXqtWLTRp0kT1tUQigb+/f5l7u7u7o6CgwOg/D1FVwkCFyEFIJBLs3r0bR48exa5du7B8+XJMnz4dJ06cUP2yX7t2Ldq2bVvmPGN9//33eO+997Bo0SK0b98eXl5e+OSTT3DixAmz552Xl4fZs2fj6aefLvOaejBQOgAQiURQKBRm31edqdeuUaMG7t+/rzHm7u5u1n2Mufe9e/dQs2ZNg9cnqopYTEvkQEQiETp06IDZs2fjzJkzcHFxwc8//4xatWohKCgIycnJqFu3rsZHRESExjWOHz+u+lwmk+H06dNo2LAhAODIkSOIjY3FW2+9hejoaNStW1dVLGquli1b4tKlS2XmVbduXY3VB30aNGiAU6dOaYzFx8drfO3i4gK5XF6uuSpFR0fjwoULGmPNmjXDoUOHUFxcbJF7qDt//jyio6Mtfl2iyoCBCpGDOHHiBObNm4dTp07h+vXr+Omnn5CRkaEKMmbPno24uDh89tlnSEpKwr///ov169dj8eLFGtdZuXIlfv75ZyQmJmLs2LG4f/8+XnnlFQBAvXr1cOrUKfz1119ISkrCBx98UCYgMNXMmTPxzTffYPbs2fjvv/9w8eJFfP/995gxY4bR13j99deRmJiI999/H0lJSdiyZQs2bNgAoCR4A0oau+Xl5WHv3r3IzMwsVyqld+/e+O+//zRWVcaNG4fc3FwMGzYMp06dwuXLl7Fx40ZcunTJ7PsAJTuSTp8+jV69epXrOkSVFQMVIgfh7e2NgwcP4qmnnkL9+vUxY8YMLFq0CH379gUAjB49GuvWrcP69evRtGlTdOnSBRs2bCizojJ//nzMnz8fzZs3x+HDh7Fjxw7UqFEDQElA8PTTT2Po0KFo27YtsrKy8NZbb5Vr3r1798avv/6KXbt2oXXr1mjXrh2WLFmCsLAwo68RERGBrVu34qeffkKzZs2watUqTJ8+HQDg6uoKAIiNjcUbb7yBoUOHombNmli4cKHZc27atClatmyJLVu2qMb8/f3x999/Iy8vD126dEGrVq2wdu1avTUrxti+fTtCQ0PRqVOncl2HqLISCYIg2HoSRGR9165dQ0REBM6cOVOu4lh7MXfuXKxevRppaWlWuf5vv/2GyZMn4/z580anqMzRrl07jB8/Hi+88ILV7kHkyFhMS0QO4fPPP0fr1q3h7++PI0eO4JNPPsG4ceOsdr9+/frh8uXLuHnzJkJCQqxyj8zMTDz99NN4/vnnrXJ9osqAKypEVYSjr6hMnDgRP/zwA+7du4fQ0FCMGDECU6dOhZMT/71FVJkxUCEiIiK7xWJaIiIislsMVIiIiMhuMVAhIiIiu8VAhYiIiOwWAxUiIiKyWwxUiIiIyG4xUCEiIiK7xUCFiIiI7Nb/A0Mh/8xarPhtAAAAAElFTkSuQmCC",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -3156,9 +3570,413 @@
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      "data": {
       "text/html": [
-       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;pf&#x27;, PolynomialFeatures(degree=10, include_bias=False)),\n",
-       "                (&#x27;lr&#x27;, LogisticRegression(fit_intercept=False))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;pf&#x27;, PolynomialFeatures(degree=10, include_bias=False)),\n",
-       "                (&#x27;lr&#x27;, LogisticRegression(fit_intercept=False))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PolynomialFeatures</label><div class=\"sk-toggleable__content\"><pre>PolynomialFeatures(degree=10, include_bias=False)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(fit_intercept=False)</pre></div></div></div></div></div></div></div>"
+       "<style>#sk-container-id-2 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-2 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-2 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-2 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;pf&#x27;, PolynomialFeatures(degree=10, include_bias=False)),\n",
+       "                (&#x27;lr&#x27;, LogisticRegression(fit_intercept=False))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;pf&#x27;, PolynomialFeatures(degree=10, include_bias=False)),\n",
+       "                (&#x27;lr&#x27;, LogisticRegression(fit_intercept=False))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;PolynomialFeatures<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.PolynomialFeatures.html\">?<span>Documentation for PolynomialFeatures</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>PolynomialFeatures(degree=10, include_bias=False)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(fit_intercept=False)</pre></div> </div></div></div></div></div></div>"
       ],
       "text/plain": [
        "Pipeline(steps=[('pf', PolynomialFeatures(degree=10, include_bias=False)),\n",
@@ -3220,7 +4038,7 @@
     {
      "data": {
       "text/plain": [
-       "<Axes: xlabel='sepal length (cm)', ylabel='sepal width (cm)'>"
+       "<AxesSubplot:xlabel='sepal length (cm)', ylabel='sepal width (cm)'>"
       ]
      },
      "execution_count": 68,
@@ -3229,7 +4047,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -3267,7 +4085,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/gurmail.singh/.local/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "/home/gurmail.singh/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
       "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
       "\n",
       "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
@@ -3280,9 +4098,413 @@
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;pf&#x27;, PolynomialFeatures(degree=5, include_bias=False)),\n",
-       "                (&#x27;lr&#x27;, LogisticRegression(fit_intercept=False))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;pf&#x27;, PolynomialFeatures(degree=5, include_bias=False)),\n",
-       "                (&#x27;lr&#x27;, LogisticRegression(fit_intercept=False))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PolynomialFeatures</label><div class=\"sk-toggleable__content\"><pre>PolynomialFeatures(degree=5, include_bias=False)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(fit_intercept=False)</pre></div></div></div></div></div></div></div>"
+       "<style>#sk-container-id-3 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-3 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-3 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-3 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-3 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;pf&#x27;, PolynomialFeatures(degree=5, include_bias=False)),\n",
+       "                (&#x27;lr&#x27;, LogisticRegression(fit_intercept=False))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;pf&#x27;, PolynomialFeatures(degree=5, include_bias=False)),\n",
+       "                (&#x27;lr&#x27;, LogisticRegression(fit_intercept=False))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;PolynomialFeatures<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.PolynomialFeatures.html\">?<span>Documentation for PolynomialFeatures</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>PolynomialFeatures(degree=5, include_bias=False)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(fit_intercept=False)</pre></div> </div></div></div></div></div></div>"
       ],
       "text/plain": [
        "Pipeline(steps=[('pf', PolynomialFeatures(degree=5, include_bias=False)),\n",
@@ -3333,12 +4555,430 @@
    "id": "48326e01",
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/gurmail.singh/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n"
+     ]
+    },
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-4 {color: black;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;pf&#x27;, PolynomialFeatures(degree=5, include_bias=False)),\n",
-       "                (&#x27;lr&#x27;, LogisticRegression(fit_intercept=False, max_iter=1000))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;pf&#x27;, PolynomialFeatures(degree=5, include_bias=False)),\n",
-       "                (&#x27;lr&#x27;, LogisticRegression(fit_intercept=False, max_iter=1000))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PolynomialFeatures</label><div class=\"sk-toggleable__content\"><pre>PolynomialFeatures(degree=5, include_bias=False)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(fit_intercept=False, max_iter=1000)</pre></div></div></div></div></div></div></div>"
+       "<style>#sk-container-id-4 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-4 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-4 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-4 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-4 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;pf&#x27;, PolynomialFeatures(degree=5, include_bias=False)),\n",
+       "                (&#x27;lr&#x27;, LogisticRegression(fit_intercept=False, max_iter=1000))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;pf&#x27;, PolynomialFeatures(degree=5, include_bias=False)),\n",
+       "                (&#x27;lr&#x27;, LogisticRegression(fit_intercept=False, max_iter=1000))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;PolynomialFeatures<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.PolynomialFeatures.html\">?<span>Documentation for PolynomialFeatures</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>PolynomialFeatures(degree=5, include_bias=False)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(fit_intercept=False, max_iter=1000)</pre></div> </div></div></div></div></div></div>"
       ],
       "text/plain": [
        "Pipeline(steps=[('pf', PolynomialFeatures(degree=5, include_bias=False)),\n",
@@ -3370,7 +5010,7 @@
     {
      "data": {
       "text/plain": [
-       "array([1, 1, 1, ..., 0, 0, 0])"
+       "array([0, 0, 0, ..., 0, 0, 0])"
       ]
      },
      "execution_count": 71,
@@ -3393,7 +5033,7 @@
     {
      "data": {
       "text/plain": [
-       "<Axes: xlabel='sepal length (cm)', ylabel='sepal width (cm)'>"
+       "<AxesSubplot:xlabel='sepal length (cm)', ylabel='sepal width (cm)'>"
       ]
      },
      "execution_count": 72,
@@ -3402,7 +5042,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -3612,11 +5252,415 @@
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      "data": {
       "text/html": [
-       "<style>#sk-container-id-5 {color: black;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;pf&#x27;, PolynomialFeatures(degree=5, include_bias=False)),\n",
+       "<style>#sk-container-id-5 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-5 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-5 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-5 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-5 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-5 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-5 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;pf&#x27;, PolynomialFeatures(degree=5, include_bias=False)),\n",
        "                (&#x27;std&#x27;, StandardScaler()),\n",
-       "                (&#x27;lr&#x27;, LogisticRegression(fit_intercept=False))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;pf&#x27;, PolynomialFeatures(degree=5, include_bias=False)),\n",
+       "                (&#x27;lr&#x27;, LogisticRegression(fit_intercept=False))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;pf&#x27;, PolynomialFeatures(degree=5, include_bias=False)),\n",
        "                (&#x27;std&#x27;, StandardScaler()),\n",
-       "                (&#x27;lr&#x27;, LogisticRegression(fit_intercept=False))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PolynomialFeatures</label><div class=\"sk-toggleable__content\"><pre>PolynomialFeatures(degree=5, include_bias=False)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(fit_intercept=False)</pre></div></div></div></div></div></div></div>"
+       "                (&#x27;lr&#x27;, LogisticRegression(fit_intercept=False))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;PolynomialFeatures<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.PolynomialFeatures.html\">?<span>Documentation for PolynomialFeatures</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>PolynomialFeatures(degree=5, include_bias=False)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(fit_intercept=False)</pre></div> </div></div></div></div></div></div>"
       ],
       "text/plain": [
        "Pipeline(steps=[('pf', PolynomialFeatures(degree=5, include_bias=False)),\n",
@@ -3673,7 +5717,7 @@
     {
      "data": {
       "text/plain": [
-       "<Axes: xlabel='sepal length (cm)', ylabel='sepal width (cm)'>"
+       "<AxesSubplot:xlabel='sepal length (cm)', ylabel='sepal width (cm)'>"
       ]
      },
      "execution_count": 81,
@@ -3682,7 +5726,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -4461,11 +6505,11 @@
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
       "Cell \u001b[0;32mIn[101], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# There are multiple recall scores as we have multiple labels\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;66;03m# So, we need to pass argument to parameter \"average\"\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m \u001b[43mrecall_score\u001b[49m\u001b[43m(\u001b[49m\u001b[43mactual\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpredicted\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/sklearn/utils/_param_validation.py:214\u001b[0m, in \u001b[0;36mvalidate_params.<locals>.decorator.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    208\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    209\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[1;32m    210\u001b[0m         skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[1;32m    211\u001b[0m             prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[1;32m    212\u001b[0m         )\n\u001b[1;32m    213\u001b[0m     ):\n\u001b[0;32m--> 214\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    215\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m InvalidParameterError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    216\u001b[0m     \u001b[38;5;66;03m# When the function is just a wrapper around an estimator, we allow\u001b[39;00m\n\u001b[1;32m    217\u001b[0m     \u001b[38;5;66;03m# the function to delegate validation to the estimator, but we replace\u001b[39;00m\n\u001b[1;32m    218\u001b[0m     \u001b[38;5;66;03m# the name of the estimator by the name of the function in the error\u001b[39;00m\n\u001b[1;32m    219\u001b[0m     \u001b[38;5;66;03m# message to avoid confusion.\u001b[39;00m\n\u001b[1;32m    220\u001b[0m     msg \u001b[38;5;241m=\u001b[39m re\u001b[38;5;241m.\u001b[39msub(\n\u001b[1;32m    221\u001b[0m         \u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter of \u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mw+ must be\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m    222\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__qualname__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must be\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m    223\u001b[0m         \u001b[38;5;28mstr\u001b[39m(e),\n\u001b[1;32m    224\u001b[0m     )\n",
-      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/sklearn/metrics/_classification.py:2304\u001b[0m, in \u001b[0;36mrecall_score\u001b[0;34m(y_true, y_pred, labels, pos_label, average, sample_weight, zero_division)\u001b[0m\n\u001b[1;32m   2144\u001b[0m \u001b[38;5;129m@validate_params\u001b[39m(\n\u001b[1;32m   2145\u001b[0m     {\n\u001b[1;32m   2146\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my_true\u001b[39m\u001b[38;5;124m\"\u001b[39m: [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124marray-like\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msparse matrix\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   2171\u001b[0m     zero_division\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwarn\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m   2172\u001b[0m ):\n\u001b[1;32m   2173\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Compute the recall.\u001b[39;00m\n\u001b[1;32m   2174\u001b[0m \n\u001b[1;32m   2175\u001b[0m \u001b[38;5;124;03m    The recall is the ratio ``tp / (tp + fn)`` where ``tp`` is the number of\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   2302\u001b[0m \u001b[38;5;124;03m    array([1. , 1. , 0.5])\u001b[39;00m\n\u001b[1;32m   2303\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 2304\u001b[0m     _, r, _, _ \u001b[38;5;241m=\u001b[39m \u001b[43mprecision_recall_fscore_support\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2305\u001b[0m \u001b[43m        \u001b[49m\u001b[43my_true\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2306\u001b[0m \u001b[43m        \u001b[49m\u001b[43my_pred\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2307\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlabels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2308\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpos_label\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpos_label\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2309\u001b[0m \u001b[43m        \u001b[49m\u001b[43maverage\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maverage\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2310\u001b[0m \u001b[43m        \u001b[49m\u001b[43mwarn_for\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrecall\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2311\u001b[0m \u001b[43m        \u001b[49m\u001b[43msample_weight\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msample_weight\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2312\u001b[0m \u001b[43m        \u001b[49m\u001b[43mzero_division\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mzero_division\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2313\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2314\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m r\n",
-      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/sklearn/utils/_param_validation.py:187\u001b[0m, in \u001b[0;36mvalidate_params.<locals>.decorator.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    185\u001b[0m global_skip_validation \u001b[38;5;241m=\u001b[39m get_config()[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mskip_parameter_validation\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m    186\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m global_skip_validation:\n\u001b[0;32m--> 187\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    189\u001b[0m func_sig \u001b[38;5;241m=\u001b[39m signature(func)\n\u001b[1;32m    191\u001b[0m \u001b[38;5;66;03m# Map *args/**kwargs to the function signature\u001b[39;00m\n",
-      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1724\u001b[0m, in \u001b[0;36mprecision_recall_fscore_support\u001b[0;34m(y_true, y_pred, beta, labels, pos_label, average, warn_for, sample_weight, zero_division)\u001b[0m\n\u001b[1;32m   1566\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Compute precision, recall, F-measure and support for each class.\u001b[39;00m\n\u001b[1;32m   1567\u001b[0m \n\u001b[1;32m   1568\u001b[0m \u001b[38;5;124;03mThe precision is the ratio ``tp / (tp + fp)`` where ``tp`` is the number of\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1721\u001b[0m \u001b[38;5;124;03m array([2, 2, 2]))\u001b[39;00m\n\u001b[1;32m   1722\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1723\u001b[0m zero_division_value \u001b[38;5;241m=\u001b[39m _check_zero_division(zero_division)\n\u001b[0;32m-> 1724\u001b[0m labels \u001b[38;5;241m=\u001b[39m \u001b[43m_check_set_wise_labels\u001b[49m\u001b[43m(\u001b[49m\u001b[43my_true\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_pred\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maverage\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpos_label\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1726\u001b[0m \u001b[38;5;66;03m# Calculate tp_sum, pred_sum, true_sum ###\u001b[39;00m\n\u001b[1;32m   1727\u001b[0m samplewise \u001b[38;5;241m=\u001b[39m average \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msamples\u001b[39m\u001b[38;5;124m\"\u001b[39m\n",
-      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1518\u001b[0m, in \u001b[0;36m_check_set_wise_labels\u001b[0;34m(y_true, y_pred, average, labels, pos_label)\u001b[0m\n\u001b[1;32m   1516\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m y_type \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmulticlass\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m   1517\u001b[0m             average_options\u001b[38;5;241m.\u001b[39mremove(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msamples\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m-> 1518\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m   1519\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTarget is \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m but average=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbinary\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m. Please \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1520\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mchoose another average setting, one of \u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m (y_type, average_options)\n\u001b[1;32m   1521\u001b[0m         )\n\u001b[1;32m   1522\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m pos_label \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m (\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m1\u001b[39m):\n\u001b[1;32m   1523\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m   1524\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNote that pos_label (set to \u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m) is ignored when \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1525\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124maverage != \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbinary\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m (got \u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m). You may use \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1528\u001b[0m         \u001b[38;5;167;01mUserWarning\u001b[39;00m,\n\u001b[1;32m   1529\u001b[0m     )\n",
+      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/sklearn/utils/_param_validation.py:213\u001b[0m, in \u001b[0;36mvalidate_params.<locals>.decorator.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    207\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    208\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[1;32m    209\u001b[0m         skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[1;32m    210\u001b[0m             prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[1;32m    211\u001b[0m         )\n\u001b[1;32m    212\u001b[0m     ):\n\u001b[0;32m--> 213\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    214\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m InvalidParameterError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    215\u001b[0m     \u001b[38;5;66;03m# When the function is just a wrapper around an estimator, we allow\u001b[39;00m\n\u001b[1;32m    216\u001b[0m     \u001b[38;5;66;03m# the function to delegate validation to the estimator, but we replace\u001b[39;00m\n\u001b[1;32m    217\u001b[0m     \u001b[38;5;66;03m# the name of the estimator by the name of the function in the error\u001b[39;00m\n\u001b[1;32m    218\u001b[0m     \u001b[38;5;66;03m# message to avoid confusion.\u001b[39;00m\n\u001b[1;32m    219\u001b[0m     msg \u001b[38;5;241m=\u001b[39m re\u001b[38;5;241m.\u001b[39msub(\n\u001b[1;32m    220\u001b[0m         \u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter of \u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mw+ must be\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m    221\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__qualname__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must be\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m    222\u001b[0m         \u001b[38;5;28mstr\u001b[39m(e),\n\u001b[1;32m    223\u001b[0m     )\n",
+      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2363\u001b[0m, in \u001b[0;36mrecall_score\u001b[0;34m(y_true, y_pred, labels, pos_label, average, sample_weight, zero_division)\u001b[0m\n\u001b[1;32m   2195\u001b[0m \u001b[38;5;129m@validate_params\u001b[39m(\n\u001b[1;32m   2196\u001b[0m     {\n\u001b[1;32m   2197\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my_true\u001b[39m\u001b[38;5;124m\"\u001b[39m: [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124marray-like\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msparse matrix\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   2222\u001b[0m     zero_division\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwarn\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m   2223\u001b[0m ):\n\u001b[1;32m   2224\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Compute the recall.\u001b[39;00m\n\u001b[1;32m   2225\u001b[0m \n\u001b[1;32m   2226\u001b[0m \u001b[38;5;124;03m    The recall is the ratio ``tp / (tp + fn)`` where ``tp`` is the number of\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   2361\u001b[0m \u001b[38;5;124;03m    array([1. , 1. , 0.5])\u001b[39;00m\n\u001b[1;32m   2362\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 2363\u001b[0m     _, r, _, _ \u001b[38;5;241m=\u001b[39m \u001b[43mprecision_recall_fscore_support\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2364\u001b[0m \u001b[43m        \u001b[49m\u001b[43my_true\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2365\u001b[0m \u001b[43m        \u001b[49m\u001b[43my_pred\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2366\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlabels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2367\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpos_label\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpos_label\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2368\u001b[0m \u001b[43m        \u001b[49m\u001b[43maverage\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maverage\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2369\u001b[0m \u001b[43m        \u001b[49m\u001b[43mwarn_for\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrecall\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2370\u001b[0m \u001b[43m        \u001b[49m\u001b[43msample_weight\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msample_weight\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2371\u001b[0m \u001b[43m        \u001b[49m\u001b[43mzero_division\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mzero_division\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2372\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2373\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m r\n",
+      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/sklearn/utils/_param_validation.py:186\u001b[0m, in \u001b[0;36mvalidate_params.<locals>.decorator.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    184\u001b[0m global_skip_validation \u001b[38;5;241m=\u001b[39m get_config()[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mskip_parameter_validation\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m    185\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m global_skip_validation:\n\u001b[0;32m--> 186\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    188\u001b[0m func_sig \u001b[38;5;241m=\u001b[39m signature(func)\n\u001b[1;32m    190\u001b[0m \u001b[38;5;66;03m# Map *args/**kwargs to the function signature\u001b[39;00m\n",
+      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1767\u001b[0m, in \u001b[0;36mprecision_recall_fscore_support\u001b[0;34m(y_true, y_pred, beta, labels, pos_label, average, warn_for, sample_weight, zero_division)\u001b[0m\n\u001b[1;32m   1604\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Compute precision, recall, F-measure and support for each class.\u001b[39;00m\n\u001b[1;32m   1605\u001b[0m \n\u001b[1;32m   1606\u001b[0m \u001b[38;5;124;03mThe precision is the ratio ``tp / (tp + fp)`` where ``tp`` is the number of\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1764\u001b[0m \u001b[38;5;124;03m array([2, 2, 2]))\u001b[39;00m\n\u001b[1;32m   1765\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1766\u001b[0m _check_zero_division(zero_division)\n\u001b[0;32m-> 1767\u001b[0m labels \u001b[38;5;241m=\u001b[39m \u001b[43m_check_set_wise_labels\u001b[49m\u001b[43m(\u001b[49m\u001b[43my_true\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_pred\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maverage\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpos_label\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1769\u001b[0m \u001b[38;5;66;03m# Calculate tp_sum, pred_sum, true_sum ###\u001b[39;00m\n\u001b[1;32m   1770\u001b[0m samplewise \u001b[38;5;241m=\u001b[39m average \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msamples\u001b[39m\u001b[38;5;124m\"\u001b[39m\n",
+      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1556\u001b[0m, in \u001b[0;36m_check_set_wise_labels\u001b[0;34m(y_true, y_pred, average, labels, pos_label)\u001b[0m\n\u001b[1;32m   1554\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m y_type \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmulticlass\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m   1555\u001b[0m             average_options\u001b[38;5;241m.\u001b[39mremove(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msamples\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m-> 1556\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m   1557\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTarget is \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m but average=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbinary\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m. Please \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1558\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mchoose another average setting, one of \u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m (y_type, average_options)\n\u001b[1;32m   1559\u001b[0m         )\n\u001b[1;32m   1560\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m pos_label \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m (\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m1\u001b[39m):\n\u001b[1;32m   1561\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m   1562\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNote that pos_label (set to \u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m) is ignored when \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1563\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124maverage != \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbinary\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m (got \u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m). You may use \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1566\u001b[0m         \u001b[38;5;167;01mUserWarning\u001b[39;00m,\n\u001b[1;32m   1567\u001b[0m     )\n",
       "\u001b[0;31mValueError\u001b[0m: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted']."
      ]
     }
@@ -4985,7 +7029,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,
diff --git a/lecture_material/22-classification/22-classification_001.ipynb b/lecture_material/22-classification/22-classification_001.ipynb
index 7393488c929fc9bc0672dac9b9c6c64975a465fb..940de57a162fd311c7fe22dde4303f9fab4aeded 100644
--- a/lecture_material/22-classification/22-classification_001.ipynb
+++ b/lecture_material/22-classification/22-classification_001.ipynb
@@ -31,6 +31,17 @@
     "from sklearn.metrics import recall_score, precision_score, balanced_accuracy_score"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "67d1ee1f-4f4f-4451-8ea9-a3ab40bcb4e1",
+   "metadata": {},
+   "source": [
+    "### IRIS dataset: http://archive.ics.uci.edu/ml/datasets/iris\n",
+    "- This set of data is used in beginning Machine Learning Courses\n",
+    "- You can train a ML algorithm to use the values to predict the class of iris\n",
+    "- Dataset link: https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -53,7 +64,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "xcols = [\"sepal length (cm)\", \"sepal width (cm)\", \"const\"]\n",
     "train, test = train_test_split(df, test_size=10, random_state=5)\n",
     "test"
    ]
@@ -2028,7 +2038,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,
diff --git a/lecture_material/22-classification/22-classification_002.ipynb b/lecture_material/22-classification/22-classification_002.ipynb
index 7393488c929fc9bc0672dac9b9c6c64975a465fb..e79e98cf5be01335f18589b984347b5f6c57f857 100644
--- a/lecture_material/22-classification/22-classification_002.ipynb
+++ b/lecture_material/22-classification/22-classification_002.ipynb
@@ -31,6 +31,17 @@
     "from sklearn.metrics import recall_score, precision_score, balanced_accuracy_score"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "91fc5b77-7a10-46e5-9b4b-d15fd7c2c276",
+   "metadata": {},
+   "source": [
+    "### IRIS dataset: http://archive.ics.uci.edu/ml/datasets/iris\n",
+    "- This set of data is used in beginning Machine Learning Courses\n",
+    "- You can train a ML algorithm to use the values to predict the class of iris\n",
+    "- Dataset link: https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -53,7 +64,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "xcols = [\"sepal length (cm)\", \"sepal width (cm)\", \"const\"]\n",
     "train, test = train_test_split(df, test_size=10, random_state=5)\n",
     "test"
    ]
@@ -2028,7 +2038,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,