Python3 使用SVM--Lasso等，训练模型，画出auc曲线

注意：roc_curve() 这个函数

来源于：sklearn.metrics.roc_curve

roc_curve(y_true, y_score, pos_label=None, sample_weight=None, drop_intermediate=True)注意它的参数：Parameters:y_true : array, shape = [n_samples]      True binary labels in range {0, 1} or {-1, 1}. If labels are not binary, pos_label should be explicitly given.y_score : array, shape = [n_samples]      Target scores, can either be probability estimates of the positive class, confidence values, or non-thresholded measure of decisions (as returned by “decision_function” on some classifiers).pos_label : int or str, default=None       Label considered as positive and others are considered negative. ...

注意第二个参数是：y_score，它可以是：对正例的概率估计值，置信度值，
决策值的非阈值测量（一些分类器中用decision_function来返回）

例如：

Model = classifier.fit(TrainX, Trainy)1.probas_ = Model.predict_proba(TestX)#一些分类器直接predict_proba，返回概率值predictions = Model.predict(TestX)#predict返回预测值fpr, tpr, thresholds = roc_curve(Testy,probas_[:,1],pos_label=1)2.probas_ = Model.decision_function(TestX)#返回