API reference# This is the full API documentation of the imbalanced-learn toolbox. Under-sampling methods Prototype generation ClusterCentroids Prototype selection CondensedNearestNeighbour EditedNearestNeighbours RepeatedEditedNearestNeighbours AllKNN InstanceHardnessThreshold NearMiss NeighbourhoodCleaningRule OneSidedSelection RandomUnderSampler TomekLinks Over-sampling methods Basic over-sampling RandomOverSampler SMOTE algorithms SMOTE SMOTENC SMOTEN ADASYN BorderlineSMOTE KMeansSMOTE SVMSMOTE Combination of over- and under-sampling methods SMOTEENN Examples using imblearn.combine.SMOTEENN SMOTETomek Examples using imblearn.combine.SMOTETomek Ensemble methods Boosting algorithms EasyEnsembleClassifier RUSBoostClassifier Bagging algorithms BalancedBaggingClassifier BalancedRandomForestClassifier Batch generator for Keras BalancedBatchGenerator Examples using imblearn.keras.BalancedBatchGenerator balanced_batch_generator Batch generator for TensorFlow balanced_batch_generator Miscellaneous FunctionSampler Examples using imblearn.FunctionSampler Pipeline Pipeline Examples using imblearn.pipeline.Pipeline make_pipeline Examples using imblearn.pipeline.make_pipeline Metrics Classification metrics classification_report_imbalanced sensitivity_specificity_support sensitivity_score specificity_score geometric_mean_score macro_averaged_mean_absolute_error make_index_balanced_accuracy Pairwise metrics ValueDifferenceMetric Datasets make_imbalance Examples using imblearn.datasets.make_imbalance fetch_datasets Examples using imblearn.datasets.fetch_datasets Utilities Validation checks used in samplers parametrize_with_checks check_neighbors_object check_sampling_strategy check_target_type Testing compatibility of your own sampler parametrize_with_checks