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AUC is the Area under the curve. This metric represents arithmetic mean of the score for each class, weighted by the number of true instances in each class.
Accuracy is the ratio of predictions that exactly match the true class labels.
The arithmetic mean of the average precision score for each class, weighted by the number of true instances in each class.
Intersection Over Union. Intersection of predictions divided by union of predictions.
Normalized macro recall is recall macro-averaged and normalized, so that random performance has a score of 0, and perfect performance has a score of 1.
The arithmetic mean of precision for each class, weighted by number of true instances in each class.
Generated using TypeDoc
Known values of ClassificationMultilabelPrimaryMetrics that the service accepts.