The sklearn's documentation of the method roc_auc_score states that the parameter multi_class can take the value 'OvR' (which stands for One-vs-Rest) or 'OvO' (which stands for One-vs-One). The question is published on by Tutorial Guruji team. My tensorflow ML algorithm gives me an ROC AUC of 0. Scikit-learn has a metrics module that provides other metrics that can be used … The answer: There is no specific threshold for what is considered a good AUC score. ![]() def multitask_auc(ground_truth, predicted): from sklearn. How to make both class and probability predictions with a final model required by the scikit-learn API. For example, if 5% of the test set are ones. The ideal score is a TPR = 1 and FPR = 0, which is the point on the top left. The accuracy was at 97% (2 misclassifications), but the ROC AUC score was 1. The ROC curve shows the trade-off between Recall (or TPR) and specificity (1 - FPR). ![]() Pastebin is a website where you can store text online for a set period of time. roc_auc_score ( y_test, y_pred) false_positive_rate, true_positive_rate, thresolds = metrics. ![]() References 1 Wikipedia entry for the Receiver operating characteristic 2 Fawcett T. ![]() One needs the predicted probabilities in order to calculate the ROC-AUC (area under the curve) score.
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