imbalanced-learn, probably, is your favorite python package that offers a number of re-sampling techniques commonly used in datasets showing strong between-class imbalance.
This is release should be be fully compatible with the latest version of
- Pipelines can cope with older versions of
- Common tests have been refactored.
- Feature warnings have been removed.
- Imposing keywords only arguments as in
imblearn.ensemble.BalancedRandomForestClassifieris expected to give different results for the same input (using the same random state).
make_index_balanced_accuracywhich was unusable due to the latest version of
- Raise a proper error message when only numerical or categorical features are given in
- Fix a bug when the median of the standard deviation is null in
min_samples_leafdefault value has been changed to be consistent with
- The classifier implemented in imbalanced-learn,
sampling_strategywith the same key than in y without the need of encoding y in advance.
You can install it either by using pip
pip install imbalanced-learn -U
or by using the conda package manager
conda update imbalanced-learn