reg test

parent d4ec2e32
......@@ -9,14 +9,19 @@
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import numpy as np"
"import numpy as np\n",
"import seaborn as sns\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn import metrics\n",
"from sklearn.metrics import accuracy_score"
]
},
{
......@@ -1112,6 +1117,13 @@
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Regression logistique"
]
},
{
"cell_type": "code",
"execution_count": 16,
......@@ -1140,7 +1152,39 @@
},
{
"cell_type": "code",
"execution_count": 22,
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fb6833aca20>"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sns.countplot(x='Status',hue='Smoker',data=raw_data, palette=[\"#E69F00\",\"#56B4E9\"])"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
......@@ -1179,6 +1223,117 @@
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
" return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fb68317f518>"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"x = df_smoker['Age']\n",
"y = df_smoker['Status']\n",
"\n",
"sns.regplot(x='Age', y='Status', data=df_smoker, logistic=True)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.6/site-packages/sklearn/utils/validation.py:578: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
" y = column_or_1d(y, warn=True)\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"X = df_smoker['Age']\n",
"y = df_smoker['Status']\n",
"\n",
"X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=0)\n",
"\n",
"X_train= X_train.values.reshape(-1, 1)\n",
"y_train= y_train.values.reshape(-1, 1)\n",
"X_test = X_test.values.reshape(-1, 1)\n",
"\n",
"logistic_regression = LogisticRegression()\n",
"logistic_regression.fit(X_train,y_train)\n",
"y_pred = logistic_regression.predict(X_test)\n",
"confusion_matrix = pd.crosstab(y_test,y_pred, rownames=['Actual'],colnames=['Predicted'])\n",
"sns.heatmap(confusion_matrix, annot=True)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(X,y)\n",
"plt.scatter(X.values.reshape(-1, 1),logistic_regression.predict_proba(X.values.reshape(-1, 1))[:,1])\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
......
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