Aggregate metrics for classification/classifier models.
For multi-class models, the metrics are either macro-averaged or
micro-averaged. When macro-averaged, the metrics are calculated for each
label and then an unweighted average is taken of those values. When
micro-averaged, the metric is calculated globally by counting the total
number of correctly predicted rows.
Recall is the fraction of actual positive labels that were
given a positive prediction. For multiclass this is a macro-
averaged metric.
Threshold at which the metrics are computed. For binary
classification models this is the positive class threshold.
For multi-class classfication models this is the confidence
threshold.
Logarithmic Loss. For multiclass this is a macro-averaged
metric.