Python：交叉验证后计算混淆矩阵

Question

我在预测模型中使用交叉验证。我想要一个计算混淆矩阵的python代码，以报告准确性，灵敏度，分类错误和特异性。

models = []
models.append(('LDA', LinearDiscriminantAnalysis()))
models.append(('CART', DecisionTreeClassifier()))
models.append(('AB', AdaBoostClassifier()))
models.append(('GBM', GradientBoostingClassifier()))
X=data[['A1', 'A2', 'A3', 'A4', 'A5']]
Y=data['class']
seed = 7
kfold = KFold(n_splits=10, random_state=seed)
for name, m in models:
    y_pred = cross_val_predict(m, X,Y, cv=kfold )
    results = cross_val_score(m, X, Y, cv=kfold)