行业知识
基于逻辑回归实现乳腺癌预测（机械学习与大数据）
2026-11-16 01:59:14
来源：互联网转载
基于逻辑回归实现乳腺癌预测
将乳腺���数据集拆分成训练集和测试集，搭建一个逻辑回归模型，对训练集进行训练，然后分别对训练集和测试集进行预测。输出以下结果：
该模型在训练集上的准确率，在测试集上的准确率、召回率和精确率。
源码
from sklearn.datasets import load_breast_cancerfrom sklearn.linear_model import LogisticRegressionfrom sklearn.model_selection import train_test_splitfrom sklearn.metrics import recall_score,precision_score,classification_report,accuracy_scorecancer = load_breast_cancer()x_train,x_test,y_train,y_test = train_test_split(cancer.data,cancer.target,test_size=0.2)model = LogisticRegression(max_iter=10000)model.fit(x_train,y_train)train_score = model.score(x_train,y_train)test_score = model.score(x_test,y_test)print("1 基于逻辑回归实现乳腺癌预测")print("李思强 20201107148")print("训练集")print("准确率：",train_score)y_pred = model.predict(x_test)accuracy_score_value = accuracy_score(y_test,y_pred)recall_score_value = recall_score(y_test,y_pred)precision_score_value = precision_score(y_test,y_pred)print("测试集")print("准确率:",accuracy_score_value)print("召回率:",recall_score_value)print("精确率:",precision_score_value) 
