This model was evaluated using K-fold cross-test.
These results are for the first fold only.
The Precision-Recall (or PR) curve illustrates the tradeoff between precision and recall at different classification thresholds.
A large area under the curve signifies both high precision (low false positive rate) and high recall (low false negative rate).
The Average Precision score approximates the area under this Precision-Recall curve.
The {{ isMulticlass() ? '(mAP) multi-class' : '(AP)' }} Average Precision score for this model is
The baseline represents the
{{ isMulticlass() ? 'proportion of the current class' : 'positive rate' }}.