===========
**October, 2018**
.. warning::
Version 0.4 is the last version of imbalanced-learn to support Python 2.7
and Python 3.4. Imbalanced-learn 0.5 will require Python 3.5 or higher.
Highlights
----------
This release brings its set of new feature as well as some API changes to
strengthen the foundation of imbalanced-learn.
As new feature, 2 new modules `imblearn.keras` and
`imblearn.tensorflow` have been added in which imbalanced-learn samplers
can be used to generate balanced mini-batches.
The module `imblearn.ensemble` has been consolidated with new classifier:
`imblearn.ensemble.BalancedRandomForestClassifier`,
`imblearn.ensemble.EasyEnsembleClassifier`,
`imblearn.ensemble.RUSBoostClassifier`.
Support for string has been added in
`imblearn.over_sampling.RandomOverSampler` and
`imblearn.under_sampling.RandomUnderSampler`. In addition, a new class
`imblearn.over_sampling.SMOTENC` allows to generate sample with data
sets containing both continuous and categorical features.
The `imblearn.over_sampling.SMOTE` has been simplified and break down
to 2 additional classes:
`imblearn.over_sampling.SVMSMOTE` and
`imblearn.over_sampling.BorderlineSMOTE`.
There is also some changes regarding the API:
the parameter ``sampling_strategy`` has been introduced to replace the
``ratio`` parameter. In addition, the ``return_indices`` argument has been
deprecated and all samplers will exposed a ``sample_indices_`` whenever this is
possible.