Over-sampling methods

The imblearn.over_sampling provides a set of method to perform over-sampling.

Basic over-sampling

RandomOverSampler(*[, sampling_strategy, ...])

Class to perform random over-sampling.

SMOTE algorithms

SMOTE(*[, sampling_strategy, random_state, ...])	Class to perform over-sampling using SMOTE.
SMOTENC(categorical_features, *[, ...])	Synthetic Minority Over-sampling Technique for Nominal and Continuous.
SMOTEN([categorical_encoder, ...])	Synthetic Minority Over-sampling Technique for Nominal.
ADASYN(*[, sampling_strategy, random_state, ...])	Oversample using Adaptive Synthetic (ADASYN) algorithm.
BorderlineSMOTE(*[, sampling_strategy, ...])	Over-sampling using Borderline SMOTE.
KMeansSMOTE(*[, sampling_strategy, ...])	Apply a KMeans clustering before to over-sample using SMOTE.
SVMSMOTE(*[, sampling_strategy, ...])	Over-sampling using SVM-SMOTE.