#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)
