逻辑回归实践

#（1）加载load_breast_cancer数据集，并划分训练集与测试集。
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
data=load_breast_cancer()
train_data, test_data, train_target, test_target = train_test_split(data[‘data‘], data[‘target‘], test_size=0.2)
#（2）训练模型，并计算训练数据集的评分数据和测试数据集的评分数据，以及查看测试样本中预测正确的个数。
from sklearn.linear_model import LogisticRegression
model=LogisticRegression()#逻辑回归模型的建立
model=model.fit(train_data,train_target)#逻辑回归模型的训练
train_score=model.score(train_data,train_target)#训练的得分
test_score=model.score(test_data,test_target)#测试得分
print(‘train score:{train_score:.6f};test score:{test_score:.6f}‘.format(train_score=train_score,test_score=test_score))
import numpy as np
y_pred=model.predict(test_data)#进行预测
print(‘matchs:{0}/{1}‘.format(np.equal(y_pred,test_target).shape[0],test_target.shape[0]))