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）
通过正则化来防止过拟合的，因为正则化可以通过增加新的额外信息,就是通过收缩的办法，限制模型变的越来越大，牺牲样本内误差，降低模型的误差，从而提高样本外的预测效果，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.metrics import classification_report
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
import numpy as np
cancer = load_breast_cancer()
x = cancer['data']
y = cancer['target']
print(x)
print(y)
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2)
model = LogisticRegression()
model.fit(x_train, y_train)
y_pre = model.predict(x_test)
print(model.score(x_test, y_test))
print('matchs:{0}/{1}'.format(np.equal(y_pre,y_test).sum(),y_test.shape[0]))
