from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report
import pandas as pd
import numpy as np
def logistic():
"""
逻辑回归做二分类进行癌症预测(根据细胞的属性特征)
:return: None
"""
# 构造列标签名字
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']
# 读数据
data = pd.read_csv("https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data", names=column)
print(data)
# 缺失值进行处理
data = data.replace(to_replace='?', value=np.nan)
data = data.dropna()
# 进行数据的分割
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
std = StandardScaler()
x_train = std.fit_transform(x_train)
x_test = std.transform(x_test)
# 逻辑回归
lg = LogisticRegression()
lg.fit(x_train, y_train)
print(lg.coef_)
y_predict = lg.predict(x_test)
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"]))
return None
if __name__ == '__main__':
logistic()
机器学习:用逻辑回归做二分类进行癌症预测
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转载自blog.csdn.net/qq_37018566/article/details/83241637
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