目录

Logistic Regression

vllbc 收录于 Sklearn

2022-09-06 约 107 字预计阅读 1 分钟次阅读

目录

导入包

import numpy as np
import pandas as pd

导入数据

data = pd.read_csv("./datasets/Social_Network_Ads.csv")
data.head()

	User ID	Gender	Age	EstimatedSalary	Purchased
0	15624510	Male	19	19000	0
1	15810944	Male	35	20000	0
2	15668575	Female	26	43000	0
3	15603246	Female	27	57000	0
4	15804002	Male	19	76000	0

X = data.iloc[:,[2,3]].values
Y = data.iloc[:,4].values

交叉验证

from sklearn.model_selection import train_test_split
X_train,X_test,Y_train,Y_test = train_test_split(X,Y,train_size=1/4,random_state=0)

标准化

from sklearn.preprocessing import StandardScaler
standardscaler = StandardScaler()
X_train = standardscaler.fit_transform(X_train)
X_test = standardscaler.transform(X_test)

训练模型

from sklearn.linear_model import LogisticRegression
model = LogisticRegression()
model.fit(X_train,Y_train)

LogisticRegression()

模型得分

model.score(X_test,Y_test)

0.7933333333333333