网站建设和备案,前端如何根据ui设计写页面,北京公司网站建设服务,wordpress加载更多基于逻辑回归实现乳腺癌预测
将乳腺癌数据集拆分成训练集和测试集#xff0c;搭建一个逻辑回归模型#xff0c;对训练集进行训练#xff0c;然后分别对训练集和测试集进行预测。输出以下结果#xff1a; 该模型在训练集上的准确率#xff0c;在测试集上的准确率、召回率和…基于逻辑回归实现乳腺癌预测
将乳腺癌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_cancer
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from 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_size0.2)
model LogisticRegression(max_iter10000)
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)运行结果
