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401 lines
18 KiB
Plaintext
401 lines
18 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "799f31eb",
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"metadata": {},
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"source": [
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"线性回归预测的是连续数值，逻辑回归预测的是离散数值。"
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]
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},
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{
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"cell_type": "markdown",
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"id": "fc3c8eef",
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"metadata": {},
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"source": [
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"线性回归： \n",
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"1.预测天气 \n",
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"2.预测股票 \n",
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"3.预测房价 \n",
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"逻辑回归： \n",
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"1.邮件是否是垃圾邮件 \n",
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"2.猫狗分类 \n",
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"3.是否贷款"
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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": 2,
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"id": "0b62f4a6",
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd"
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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": 3,
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"id": "efe20171",
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"metadata": {},
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"outputs": [],
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"source": [
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"df = pd.read_csv(\"insurance_data.csv\")"
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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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"id": "a0336e69",
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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>age</th>\n",
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" <th>bought_insurance</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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" <th>0</th>\n",
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" <td>22</td>\n",
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" <td>0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>25</td>\n",
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" <td>0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>47</td>\n",
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" <td>1</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>52</td>\n",
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" <td>0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>46</td>\n",
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" <td>1</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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" age bought_insurance\n",
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"0 22 0\n",
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"1 25 0\n",
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"2 47 1\n",
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"3 52 0\n",
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"4 46 1"
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]
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},
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"execution_count": 5,
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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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"df.head()"
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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": 6,
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"id": "50fb949d",
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"metadata": {},
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"outputs": [],
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"source": [
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"%matplotlib inline\n",
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"import matplotlib.pyplot as plt"
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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": 7,
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"id": "e77e37f4",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<matplotlib.collections.PathCollection at 0x2716f2d0608>"
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]
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},
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"execution_count": 7,
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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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"data": {
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"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"plt.scatter(df.age, df.bought_insurance, marker=\"+\", color='r')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 8,
|
|
"id": "bf219340",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"(27, 2)"
|
|
]
|
|
},
|
|
"execution_count": 8,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"df.shape"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"id": "3a6ed8a7",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"from sklearn.model_selection import train_test_split"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"id": "c85065da",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"x_train, x_test, y_train, y_test = train_test_split(df[['age']], df.bought_insurance, train_size=0.9, random_state=10)"
|
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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": 11,
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"id": "16437565",
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"metadata": {},
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"outputs": [
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{
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|
"data": {
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"text/plain": [
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"(24, 3)"
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|
]
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},
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"execution_count": 11,
|
|
"metadata": {},
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"output_type": "execute_result"
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|
}
|
|
],
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"source": [
|
|
"len(x_train), len(x_test)"
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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": 12,
|
|
"id": "4d83ec8c",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"from sklearn.linear_model import LogisticRegression"
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|
]
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},
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{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
|
|
"id": "61312ccd",
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|
"metadata": {},
|
|
"outputs": [],
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|
"source": [
|
|
"lr = LogisticRegression()"
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|
]
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|
},
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|
{
|
|
"cell_type": "code",
|
|
"execution_count": 14,
|
|
"id": "dac8d670",
|
|
"metadata": {},
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|
"outputs": [
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{
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|
"data": {
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"text/plain": [
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"LogisticRegression()"
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]
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},
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"execution_count": 14,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"lr.fit(x_train, y_train)"
|
|
]
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|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 18,
|
|
"id": "698480b6",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"(array([1, 1, 0], dtype=int64),\n",
|
|
" 7 1\n",
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" 5 1\n",
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" 18 0\n",
|
|
" Name: bought_insurance, dtype: int64)"
|
|
]
|
|
},
|
|
"execution_count": 18,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"lr.predict(x_test), y_test"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 15,
|
|
"id": "89c91628",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"1.0"
|
|
]
|
|
},
|
|
"execution_count": 15,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"lr.score(x_test, y_test)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 19,
|
|
"id": "452bb440",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"array([[0.06470723, 0.93529277],\n",
|
|
" [0.10327405, 0.89672595],\n",
|
|
" [0.92775095, 0.07224905]])"
|
|
]
|
|
},
|
|
"execution_count": 19,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"lr.predict_proba(x_test)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 23,
|
|
"id": "1246d4de",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"D:\\envs\\stark-lin\\lib\\site-packages\\sklearn\\base.py:451: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
|
|
" \"X does not have valid feature names, but\"\n",
|
|
"D:\\envs\\stark-lin\\lib\\site-packages\\sklearn\\base.py:451: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
|
|
" \"X does not have valid feature names, but\"\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"(array([1], dtype=int64), array([0], dtype=int64))"
|
|
]
|
|
},
|
|
"execution_count": 23,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"lr.predict([[53]]), lr.predict([[20]])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "bc340fea",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.8.0"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|