DEV Community 👩‍💻👨‍💻

Chris Aridas
Chris Aridas

Posted on

imbalanced-learn 0.7.0 is out

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 scikit-learn.

Maintenance

  • Pipelines can cope with older versions of joblib.
  • Common tests have been refactored.
  • Feature warnings have been removed.
  • Imposing keywords only arguments as in scikit-learn.

Changed models

  • imblearn.ensemble.BalancedRandomForestClassifier is expected to give different results for the same input (using the same random state).
  • Fix make_index_balanced_accuracy which was unusable due to the latest version of scikit-learn.
  • Raise a proper error message when only numerical or categorical features are given in imblearn.over_sampling.SMOTENC.
  • Fix a bug when the median of the standard deviation is null in imblearn.over_sampling.SMOTENC.

Bug fixes

  • min_samples_leaf default value has been changed to be consistent with scikit-learn ### Enhancements
  • The classifier implemented in imbalanced-learn, imblearn.ensemble.BalancedBaggingClassifier, imblearn.ensemble.BalancedRandomForestClassifier, imblearn.ensemble.EasyEnsembleClassifier, and imblearn.ensemble.RUSBoostClassifier, accept sampling_strategy with the same key than in y without the need of encoding y in advance.
  • Import keras module lazily.

Installation

You can install it either by using pip

pip install imbalanced-learn -U
Enter fullscreen mode Exit fullscreen mode

or by using the conda package manager

conda update imbalanced-learn
Enter fullscreen mode Exit fullscreen mode

The changelog can be found here, while installation instructions, API documentation, examples and a user guide can be found here.

Happy hacking,
Chris

Top comments (0)

All DEV content is created by the community!

Hey, if you're landing here for the first time, you should know that this website is a global community of folks who blog about their experiences to help folks like you out.

Sign up now if you're curious. It's free!