LR逻辑回归附代码

参考https://zhuanlan.zhihu.com/p/74874291

 

 

import pandas as pd
pd.set_option('display.max_columns', 500)
import zipfile
with zipfile.ZipFile('KaggleCredit2.csv.zip', 'r') as z:   ##读取zip里的文件
    f = z.open('KaggleCredit2.csv')
    data = pd.read_csv(f, index_col=0)
data.head()

data.isnull().sum(axis=0)

data.dropna(inplace=True)   ##去掉为空的数据
data.shape

y = data['SeriousDlqin2yrs']
X = data.drop('SeriousDlqin2yrs', axis=1)

y.mean() ##求取均值

from sklearn import model_selection
x_tran,x_tpe)est,y_tran,y_test=model_selection.train_test_split(X,y,test_size=0.2)
print(x_test.sha

from sklearn.linear_model import LogisticRegression
## https://blog.csdn.net/sun_shengyun/article/details/53811483
lr=LogisticRegression(multi_class='ovr',solver='sag',class_weight='balanced')
lr.fit(x_tran,y_tran)
score=lr.score(x_tran,y_tran)
print(score) ##最好的分数是1

from sklearn.metrics import accuracy_score
## https://blog.csdn.net/qq_16095417/article/details/79590455
train_score=accuracy_score(y_tran,lr.predict(x_tran))
test_score=lr.score(x_test,y_test)
print('训练集准确率：',train_score)
print('测试集准确率：',test_score)

##召回率
from sklearn.metrics import recall_score
train_recall=recall_score(y_tran,lr.predict(x_tran),average='macro')
test_recall=recall_score(y_test,lr.predict(x_test),average='macro')
print('训练集召回率：',train_recall)
print('测试集召回率：',test_recall)

import numpy as np
y_pro=lr.predict_proba(x_test) ##获取预测概率值
y_prd2 = [list(p>=0.3).index(1) for i,p in enumerate(y_pro)]   ##设定0.3阈值，把大于0.3的看成1分类。
train_score=accuracy_score(y_test,y_prd2)
print(train_score)