第十五节 逻辑回归

逻辑回归是二分类问题，与其他回归不同，逻辑回归可以给出确切的概率值，哪个类别的数量少，判定概率值就是指的这个类别，这个类别即是正例

逻辑回归的损失函数称为对数似然损失函数，但其只能通过梯度下降法求解

逻辑回归sklearn的API：from sklearn.linear_model import LogisticRegression

from sklearn.linear_model import LogisticRegression
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import classification_report

# 数据地址http://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/
def logistic():
    '''逻辑回归做二分类进行癌症预测'''
    column = ['sample code number', 'clump thickness', 'uniformity of cell size', 'uniformity of cell shape', 'marginal adhesion', 'single epithelial cell size', 'bare nuclei', 'bland chromatin', 'normal nucleoli', 'mitoses', 'class']
    data = pd.read_csv(r"E:360DownloadsSoftwarereast-cancer-wisconsin.data", names=column)
    # print(data)

    # 处理缺失值
    data = data.replace(to_replace="?", value=np.nan)
    data = data.dropna(axis=0, how='any')

    # 进行数据的分割，x特征值，y目标值
    x_train, x_test, y_train, y_test = train_test_split(data[column[1:10]], data[column[10]], test_size=0.25)

    # 进行标准化处理
    x_std = StandardScaler()
    x_train = x_std.fit_transform(x_train)
    x_test = x_std.fit_transform(x_test)

    # 进行逻辑回归预测，C正则化力度，可以通过网格搜索调优
    lg = LogisticRegression(C=1.0)
    lg.fit(x_train, y_train)
    y_predict = lg.predict(x_test)
    print(lg.coef_)
    print("准确率:", lg.score(x_test, y_test))
    print("召回率：", classification_report(y_test, y_predict, labels=[2, 4], target_names=["良性", "恶性"]))


if __name__ == "__main__":
    logistic()