Logistic回归模型，python

代码参考https://blog.csdn.net/DL11007/article/details/129204192?ops_request_misc=&request_id=&biz_id=102&utm_term=logistic%E6%A8%A1%E5%9E%8Bpython&utm_medium=distribute.pc_search_result.none-task-blog-2~all~sobaiduweb~default-1-129204192.142^v88^control_2,239^v2^insert_chatgpt&spm=1018.2226.3001.4187

df.loc[]和df.iloc[]：https://blog.csdn.net/weixin_48701352/article/details/120247544?ops_request_misc=%257B%2522request%255Fid%2522%253A%2522168864051316800182741570%2522%252C%2522scm%2522%253A%252220140713.130102334..%2522%257D&request_id=168864051316800182741570&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2~all~top_click~default-2-120247544-null-null.142^v88^control_2,239^v2^insert_chatgpt&utm_term=df.iloc&spm=1018.2226.3001.4187

 后续报错：

ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT

解决方法：增加迭代次数

参考：https://blog.csdn.net/qq_43391414/article/details/113144702

 

代码：

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
data=pd.read_pickle('ICC_rms.pkl')
df=pd.DataFrame(data)
X = df.iloc[:, 0:510].values #所有样本的x值，0-510列
y = df.iloc[:, 511].values #所有样本的标签，511列

#划分数据集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=0)

#对数据进行特征提取，进行数据标准化
sc_X = StandardScaler()
X_train = sc_X.fit_transform(X_train)
X_test = sc_X.transform(X_test)

#Logistic拟合模型，max_iter=1000即增加迭代次数
classifier = LogisticRegression(max_iter=1000)
classifier.fit(X_train, y_train)

#打印参数结果
print("Logistic参数结果：",classifier.intercept_,classifier.coef_)

y_pred = classifier.predict(X_test)
# 计算R方
cm = accuracy_score(y_test, y_pred)

print("测试集的R方：", cm)