测试了一下使用linear regression做分类任务准确率的确很高啊，我的的思路是：

1. 需要预测的是 0 1 ，而linear regression本来的预测值是连续变量
2. 把linear regression预测的结果 >0.5 的当成 1，把 <0.5 的当成0
3. 然后把预测结果与实际的结果比较

核心代码（交叉验证）

这里根据自己的数据填充：

• x_train_std：正则化后的训练的 X
• y_train：训练的 Y

import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import ShuffleSplit
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import KFold
from sklearn.metrics import make_scorer
from sklearn.model_selection import cross_validate

def linear_score(true_value, predict):
    predict[predict < 0.5] = 0
    predict[predict > 0.5] = 1
    return predict[predict == true_value].size / predict.size

liner_model = LinearRegression()
scoring = {
    'linear_score': make_scorer(linear_score, greater_is_better=True)
}
kfold = KFold(n_splits=10, random_state=0)
cv_cross = cross_validate(liner_model, x_train_std, y_train, cv=kfold, scoring=scoring)

print(cv_cross['test_linear_score'].mean()) # 交叉验证的均值
print(cv_cross['test_linear_score'].std()) # 交叉验证的方差