逻辑回归实践

1.逻辑回归是怎么防止过拟合的?为什么正则化可以防止过拟合?(大家用自己的话介绍下)

答:正则化;在过拟合的情况下,拟合函数的系数一般非常大,正则化约束参数的范数不过大,从而防止过拟合。

2.用logiftic回归来进行实践操作,数据不限。

from sklearn.datasets import load_breast_cancer
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
import numpy as np
data = load_breast_cancer()# 载入load_breast_cancer数据集
x = data['data']
y = data['target']
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3)
LR_model = LogisticRegression()  # 构建逻辑回归模型
LR_model.fit(x_train, y_train)   # 训练模型
pre = LR_model.predict(x_test)   # 预测模型
print('训练数据集评分:', LR_model.score(x_train, y_train))
print('测试数据集评分:', LR_model.score(x_test, y_test))
print('测试样本的个数:',x_test.shape[0])
print('测试样本中预测正确个数:',x_test.shape[0]*LR_model.score(x_test,y_test))
print("召回率:", classification_report(y_test, pre))

运行结果如图:

逻辑回归实践

 

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