Metrics

The imblearn.metrics module includes score functions, performance metrics and pairwise metrics and distance computations.

Classification metrics

See the Metrics section of the user guide for further details.

classification_report_imbalanced(y_true, ...)	Build a classification report based on metrics used with imbalanced dataset.
sensitivity_specificity_support(y_true, ...)	Compute sensitivity, specificity, and support for each class.
sensitivity_score(y_true, y_pred, *[, ...])	Compute the sensitivity.
specificity_score(y_true, y_pred, *[, ...])	Compute the specificity.
geometric_mean_score(y_true, y_pred, *[, ...])	Compute the geometric mean.
macro_averaged_mean_absolute_error(y_true, ...)	Compute Macro-Averaged MAE for imbalanced ordinal classification.
make_index_balanced_accuracy(*[, alpha, squared])	Balance any scoring function using the index balanced accuracy.

Pairwise metrics

See the Pairwise metrics section of the user guide for further details.

Metrics to perform pairwise computation.

ValueDifferenceMetric(*[, n_categories, k, r])

Class implementing the Value Difference Metric.