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 SMOTEENN Examples using imblearn.combine.SMOTEENN SMOTETomek SMOTETomek Examples using imblearn.combine.SMOTETomek Ensemble methods Boosting algorithms EasyEnsembleClassifier RUSBoostClassifier Bagging algorithms BalancedBaggingClassifier BalancedRandomForestClassifier Batch generator for Keras BalancedBatchGenerator BalancedBatchGenerator Examples using imblearn.keras.BalancedBatchGenerator balanced_batch_generator balanced_batch_generator Batch generator for TensorFlow balanced_batch_generator balanced_batch_generator Miscellaneous FunctionSampler FunctionSampler Examples using imblearn.FunctionSampler Pipeline Pipeline Pipeline Examples using imblearn.pipeline.Pipeline make_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 make_imbalance Examples using imblearn.datasets.make_imbalance fetch_datasets 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