首先，roc_auc_score函数需要具有相同形状的输入参数。sklearn.metrics.roc_auc_score(y_true, y_score, average=’macro’, sample_weight=None)
Note: this implementation is restricted to the binary classification task or multilabel classification task in label indicator format.
y_true : array, shape = [n_samples] or [n_samples, n_classes]
True binary labels in binary label indicators.
y_score : array, shape = [n_samples] or [n_samples, n_classes]
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).
现在，输入的是真实和预测的分数，而不是您在发布的示例中使用的培训和标签数据。详细信息，model.fit(X_important_train, y_train)
model.score(X_important_train, y_train)
# this is wrong here
roc_auc_score(X_important_train, y_train)
你应该这样做：y_pred = model.predict(X_test_data)
roc_auc_score(y_true, y_pred)