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 ofscikit-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 withscikit-learn
### Enhancements - The classifier implemented in imbalanced-learn,
imblearn.ensemble.BalancedBaggingClassifier
,imblearn.ensemble.BalancedRandomForestClassifier
,imblearn.ensemble.EasyEnsembleClassifier
, andimblearn.ensemble.RUSBoostClassifier
, acceptsampling_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
or by using the conda package manager
conda update imbalanced-learn
The changelog can be found here, while installation instructions, API documentation, examples and a user guide can be found here.
Happy hacking,
Chris
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