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DOC: add warning about limitations of oversampling methods #1165

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12 changes: 10 additions & 2 deletions doc/over_sampling.rst
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Expand Up @@ -101,9 +101,17 @@ From random over-sampling to SMOTE and ADASYN
Apart from the random sampling with replacement, there are two popular methods
to over-sample minority classes: (i) the Synthetic Minority Oversampling
Technique (SMOTE) :cite:`chawla2002smote` and (ii) the Adaptive Synthetic
(ADASYN) :cite:`he2008adasyn` sampling method. These algorithms can be used in
the same manner::
(ADASYN) :cite:`he2008adasyn` sampling method.
.. warning::

Oversampling techniques such as SMOTE and ADASYN can help balance training
datasets but they do not necessarily improve ranking metrics such as
ROC-AUC. In some cases they may also negatively affect probability
calibration. Users should therefore carefully evaluate model performance
when applying oversampling methods.

These algorithms can be used in the same manner::

>>> from imblearn.over_sampling import SMOTE, ADASYN
>>> X_resampled, y_resampled = SMOTE().fit_resample(X, y)
>>> print(sorted(Counter(y_resampled).items()))
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