AI机器学习之sklearn实现logistic回归

下面用sklearn实现logistic回归：

因为logistic回归是一个二分类，所以这次使用的数据是乳腺癌的数据（sklearn自带数据）

from sklearn.datasets import load_breast_cancer
from sklearn.linear_model import LogisticRegression

breast_cancer = load_breast_cancer()
from sklearn.model_selection import train_test_split
# print(diabetes)

diabetes_x = breast_cancer.data
diabetes_y = breast_cancer.target
# print(diabetes_x)
# print(diabetes_y)

x_train,x_test,y_train,y_test =train_test_split(diabetes_x, diabetes_y, test_size=0.3)

log = LogisticRegression()
log.fit(x_train,y_train)

#
# print(log.predict(x_test))
# print(y_test)
count = 0
l = len(y_test)
print(l)
for i in range(l):
if log.predict(x_test)[i]!=y_test[i]:
count+=1
print(count)

print(1-count/l)#输出准确率