机器学习—逻辑回归

理论部分：https://www.cnblogs.com/pinard/p/6029432.html
sklearn中逻辑回归相关参数：https://www.cnblogs.com/pinard/p/6035872.html
对是否患癌症进行预测：

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
cancer_data=load_breast_cancer()     #导入数据
sample=cancer_data.data              #特征数据
label=cancer_data.target             #标签数据
X_train,X_test,y_train,y_test=train_test_split(sample,label,test_size=0.3)  #划分数据集
lr=LogisticRegression()                  #调用逻辑回归
lr.fit(X_train,y_train)                  #用测试集训练模型
print(lr.coef_)                            #系数
y_predict=lr.predict(X_test)                 #预测
print("准确率：",lr.score(X_test,y_test))    #准确率
print(classification_report(y_test,y_predict,labels=[0,1],target_names=["恶性","良性"])) #精确率、召回率、F1

运行结果：