Under-sampling methods

The imblearn.under_sampling provides methods to under-sample a dataset.

Prototype generation

The imblearn.under_sampling.prototype_generation submodule contains methods that generate new samples in order to balance the dataset.

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

Undersample by generating centroids based on clustering methods.

Prototype selection

The imblearn.under_sampling.prototype_selection submodule contains methods that select samples in order to balance the dataset.

CondensedNearestNeighbour(*[, ...])	Undersample based on the condensed nearest neighbour method.
EditedNearestNeighbours(*[, ...])	Undersample based on the edited nearest neighbour method.
RepeatedEditedNearestNeighbours(*[, ...])	Undersample based on the repeated edited nearest neighbour method.
AllKNN(*[, sampling_strategy, n_neighbors, ...])	Undersample based on the AllKNN method.
InstanceHardnessThreshold(*[, estimator, ...])	Undersample based on the instance hardness threshold.
NearMiss(*[, sampling_strategy, version, ...])	Class to perform under-sampling based on NearMiss methods.
NeighbourhoodCleaningRule(*[, ...])	Undersample based on the neighbourhood cleaning rule.
OneSidedSelection(*[, sampling_strategy, ...])	Class to perform under-sampling based on one-sided selection method.
RandomUnderSampler(*[, sampling_strategy, ...])	Class to perform random under-sampling.
TomekLinks(*[, sampling_strategy, n_jobs])	Under-sampling by removing Tomek's links.