List of preformated metrics

Evaluation metrics

If you use the compute_metrics function in the classification.compute_metrics or regression.compute_metrics functions there are preformated metrics.

You can see details of the code in the documentation page : transparentai.models

This is the list :

Problem type	metric name
classification	'accuracy'
classification	'balanced_accuracy'
classification	'average_precision'
classification	'brier_score'
classification	'f1'
classification	'f1_micro'
classification	'f1_macro'
classification	'f1_weighted'
classification	'f1_samples'
classification	'log_loss'
classification	'precision'
classification	'precision_micro'
classification	'recall'
classification	'recall_micro'
classification	'true_positive_rate'
classification	'false_positive_rate'
classification	'jaccard'
classification	'matthews_corrcoef'
classification	'roc_auc'
classification	'roc_auc_ovr'
classification	'roc_auc_ovo'
classification	'roc_auc_ovr_weighted'
classification	'roc_auc_ovo_weighted'
classification	'true_positives'
classification	'false_positives'
classification	'false_negatives'
classification	'true_negatives'
classification	'confusion_matrix'
regression	'max_error'
regression	'mean_absolute_error'
regression	'mean_squared_error'
regression	'root_mean_squared_error'
regression	'mean_squared_log_error'
regression	'median_absolute_error'
regression	'r2'
regression	'mean_poisson_deviance'
regression	'mean_gamma_deviance'