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Adding nonessential code for MLOpsPython BYOC Azure doc (#163)
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‎experimentation/Diabetes Ridge Regression Training.ipynb

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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
@@ -36,13 +36,227 @@
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"X, y = load_diabetes(return_X_y=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"(442, 10)\n"
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]
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}
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],
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"source": [
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"print(X.shape)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"(442,)\n"
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]
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}
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],
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"source": [
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"print(y.shape)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>0</th>\n",
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" <th>1</th>\n",
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" <th>2</th>\n",
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" <th>3</th>\n",
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" <th>4</th>\n",
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" <th>5</th>\n",
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" <th>6</th>\n",
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" <th>7</th>\n",
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" <th>8</th>\n",
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" <th>9</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <td>count</td>\n",
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" <td>4.420000e+02</td>\n",
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" <td>4.420000e+02</td>\n",
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" <td>4.420000e+02</td>\n",
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" <td>4.420000e+02</td>\n",
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" <td>4.420000e+02</td>\n",
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" <td>4.420000e+02</td>\n",
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" <td>4.420000e+02</td>\n",
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" <td>4.420000e+02</td>\n",
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" <td>4.420000e+02</td>\n",
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" <td>4.420000e+02</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>mean</td>\n",
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" <td>-3.639623e-16</td>\n",
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" <td>1.309912e-16</td>\n",
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" <td>-8.013951e-16</td>\n",
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" <td>1.289818e-16</td>\n",
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" <td>-9.042540e-17</td>\n",
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" <td>1.301121e-16</td>\n",
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" <td>-4.563971e-16</td>\n",
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" <td>3.863174e-16</td>\n",
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" <td>-3.848103e-16</td>\n",
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" <td>-3.398488e-16</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>std</td>\n",
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" <td>4.761905e-02</td>\n",
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" <td>4.761905e-02</td>\n",
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" <td>4.761905e-02</td>\n",
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" <td>4.761905e-02</td>\n",
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" <td>4.761905e-02</td>\n",
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" <td>4.761905e-02</td>\n",
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" <td>4.761905e-02</td>\n",
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" <td>4.761905e-02</td>\n",
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" <td>4.761905e-02</td>\n",
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" <td>4.761905e-02</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>min</td>\n",
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" <td>-1.072256e-01</td>\n",
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" <td>-4.464164e-02</td>\n",
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" <td>-9.027530e-02</td>\n",
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" <td>-1.123996e-01</td>\n",
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" <td>-1.267807e-01</td>\n",
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" <td>-1.156131e-01</td>\n",
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" <td>-1.023071e-01</td>\n",
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" <td>-7.639450e-02</td>\n",
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" <td>-1.260974e-01</td>\n",
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" <td>-1.377672e-01</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>25%</td>\n",
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" <td>-3.729927e-02</td>\n",
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" <td>-4.464164e-02</td>\n",
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" <td>-3.422907e-02</td>\n",
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" <td>-3.665645e-02</td>\n",
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" <td>-3.424784e-02</td>\n",
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" <td>-3.035840e-02</td>\n",
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" <td>-3.511716e-02</td>\n",
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" <td>-3.949338e-02</td>\n",
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" <td>-3.324879e-02</td>\n",
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" <td>-3.317903e-02</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>50%</td>\n",
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" <td>5.383060e-03</td>\n",
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" <td>-4.464164e-02</td>\n",
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" <td>-7.283766e-03</td>\n",
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" <td>-5.670611e-03</td>\n",
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" <td>-4.320866e-03</td>\n",
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" <td>-3.819065e-03</td>\n",
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" <td>-6.584468e-03</td>\n",
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" <td>-2.592262e-03</td>\n",
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" <td>-1.947634e-03</td>\n",
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" <td>-1.077698e-03</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>75%</td>\n",
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" <td>3.807591e-02</td>\n",
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" <td>5.068012e-02</td>\n",
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" <td>3.124802e-02</td>\n",
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" <td>3.564384e-02</td>\n",
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" <td>2.835801e-02</td>\n",
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" <td>2.984439e-02</td>\n",
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" <td>2.931150e-02</td>\n",
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" <td>3.430886e-02</td>\n",
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" <td>3.243323e-02</td>\n",
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" <td>2.791705e-02</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>max</td>\n",
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" <td>1.107267e-01</td>\n",
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" <td>5.068012e-02</td>\n",
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" <td>1.705552e-01</td>\n",
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" <td>1.320442e-01</td>\n",
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" <td>1.539137e-01</td>\n",
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" <td>1.987880e-01</td>\n",
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" <td>1.811791e-01</td>\n",
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" <td>1.852344e-01</td>\n",
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" <td>1.335990e-01</td>\n",
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" <td>1.356118e-01</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" 0 1 2 3 4 \\\n",
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"count 4.420000e+02 4.420000e+02 4.420000e+02 4.420000e+02 4.420000e+02 \n",
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"mean -3.639623e-16 1.309912e-16 -8.013951e-16 1.289818e-16 -9.042540e-17 \n",
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"std 4.761905e-02 4.761905e-02 4.761905e-02 4.761905e-02 4.761905e-02 \n",
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"min -1.072256e-01 -4.464164e-02 -9.027530e-02 -1.123996e-01 -1.267807e-01 \n",
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"25% -3.729927e-02 -4.464164e-02 -3.422907e-02 -3.665645e-02 -3.424784e-02 \n",
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"50% 5.383060e-03 -4.464164e-02 -7.283766e-03 -5.670611e-03 -4.320866e-03 \n",
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"75% 3.807591e-02 5.068012e-02 3.124802e-02 3.564384e-02 2.835801e-02 \n",
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"max 1.107267e-01 5.068012e-02 1.705552e-01 1.320442e-01 1.539137e-01 \n",
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"\n",
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" 5 6 7 8 9 \n",
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"count 4.420000e+02 4.420000e+02 4.420000e+02 4.420000e+02 4.420000e+02 \n",
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"mean 1.301121e-16 -4.563971e-16 3.863174e-16 -3.848103e-16 -3.398488e-16 \n",
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"std 4.761905e-02 4.761905e-02 4.761905e-02 4.761905e-02 4.761905e-02 \n",
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"min -1.156131e-01 -1.023071e-01 -7.639450e-02 -1.260974e-01 -1.377672e-01 \n",
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"25% -3.035840e-02 -3.511716e-02 -3.949338e-02 -3.324879e-02 -3.317903e-02 \n",
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"50% -3.819065e-03 -6.584468e-03 -2.592262e-03 -1.947634e-03 -1.077698e-03 \n",
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"75% 2.984439e-02 2.931150e-02 3.430886e-02 3.243323e-02 2.791705e-02 \n",
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"max 1.987880e-01 1.811791e-01 1.852344e-01 1.335990e-01 1.356118e-01 "
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]
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import pandas as pd\n",
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"features = pd.DataFrame(X)\n",
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"features.describe()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},

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