#支持向量机

from sklearn.metrics import classification_report

# 首先计算预测值

pre_cla = classifier.predict(X_test)

report_cla = classification_report(Y_test,pre_cla)

print(report_cla)

#随机森林

from sklearn.metrics import classification_report

# 首先计算预测值

pre_rf = rf_classifier.predict(X_test)

report_rf = classification_report(Y_test,pre_rf)

print(report_rf)

#逻辑回归

from sklearn.metrics import classification_report

# 首先计算预测值

pre_lr = lr_classifier.predict(X_test)

report_lr = classification_report(Y_test,pre_lr)

print(report_lr)

#K-NN近邻算法

from sklearn.metrics import classification_report

# 首先计算预测值

pre_knn = knn_classifier.predict(X_test)

report_knn = classification_report(Y_test,pre_knn)

print(report_knn)

#贝叶斯

from sklearn.metrics import classification_report

# 首先计算预测值

pre_gau = gau_classifier.predict(X_test)

report_gau = classification_report(Y_test,pre_gau)

print(report_gau)