roc_auc_score
roc_auc_score# sklearn.metrics.roc_auc_score(y_true, y_score, *, average='macro', sample_weight=None, max_fpr=None, multi_class='raise', labels=None)[source]# Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC) from prediction scores. Note: this implementation can be used with binary, multiclass and multilabel classification, but some restrictions apply (see Parameters). Read
3.4. Metrics and scoring: quantifying the quality of predictions
3.4. Metrics and scoring: quantifying the quality of predictions# 3.4.1. Which scoring function should I use?# Before we take a closer look into the details of the many scores and evaluation metrics, we want to give some guidance, inspired by statistical decision theory, on the choice of scoring functions for supervised learning, see [Gneiting2009]: Which scoring function should I use? Which scori
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roc_curve
