多个二分类模型效果混淆矩阵展示_二分类数据集怎么用混淆矩阵代码

data_for_model_ok = data[data.tag==0]
data_for_model_ng = data[data.tag==1]
 
data_for_model_ng_train = data_for_model_ng[:40]
data_for_model_ng_test = data_for_model_ng[40:]
 
data_for_model_ok_train = data_for_model_ok[:3000]
data_for_model_ok_test = data_for_model_ok[3000:]
 
data_train = pd.concat([data_for_model_ng_train,data_for_model_ok_train], axis=0)
data_test = pd.concat([data_for_model_ng_test,data_for_model_ok_test], axis=0)
 
data_train_x = data_train.drop(columns='tag').drop(columns='b')
data_train_y = data_train['tag']
 
data_test_x = data_test.drop(columns='tag').drop(columns='b')
data_test_y = data_test['tag']
 
MLbox = [AdaBoostClassifier,BaggingClassifier,ExtraTreesClassifier,GradientBoostingClassifier,
        RandomForestClassifier,HistGradientBoostingClassifier]
 
for each in MLbox:
    MODEL = each()
    MODEL.fit(data_train_x,data_train_y)
    data_all['pre_tag'] = MODEL.predict(data_all[feature])
    print('_____________________')
    print(each)
    print(confusion_matrix(data_all['tag'],data_all['pre_tag']))

<class 'sklearn.ensemble._weight_boosting.AdaBoostClassifier'>
[[5167   17]
 [  12   42]]
_____________________
<class 'sklearn.ensemble._bagging.BaggingClassifier'>
[[5169   15]
 [   5   49]]
_____________________
<class 'sklearn.ensemble._forest.ExtraTreesClassifier'>
[[5180    4]
 [   2   52]]
_____________________
<class 'sklearn.ensemble._gb.GradientBoostingClassifier'>
[[5171   13]
 [   3   51]]
_____________________
<class 'sklearn.ensemble._forest.RandomForestClassifier'>
[[5175    9]
 [   1   53]]
_____________________
<class 'sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier'>
[[5181    3]
 [  13   41]]

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