机器学习-分类算法-逻辑回归

# -*- coding: utf-8 -*-
"""
Spyder Editor

This is a temporary script file.
"""

import matplotlib.pyplot as plt  
import numpy as np  
from sklearn.model_selection import train_test_split   
from sklearn import datasets, linear_model

def laod_data():
    iris=datasets.load_iris()
    X_train=iris.data
    y_train=iris.target
    return train_test_split(X_train,y_train,
    test_size=0.3,random_state=0,stratify=y_train)#stratify分层
    
def test_LogisticRegression(*data):
    X_train,X_test,y_train,y_test=data
    regr=linear_model.LogisticRegression(solver=‘liblinear‘)
    regr.fit(X_train,y_train)
    print(‘Coefficients:%s, intercept %s‘%(regr.coef_,regr.intercept_))
    print("Score:%.2f"%regr.score(X_test,y_test))
    
if __name__==‘__main__‘:
    X_train,X_test,y_train,y_test=laod_data() 
    test_LogisticRegression(X_train,X_test,y_train,y_test)