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Add imbalanced-learn

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Added imbalanced-learn to Python / General-Purpose Machine Learning

\[Awesome\] imbalanced-learn is part of [scikit-learn-contrib](https://github.com/scikit-learn-contrib), fully compatible with scikit-learn, and provides several techniques for handling imbalanced data, including under sampling, over sampling, and ensembling.

On the (github page)[https://github.com/scikit-learn-contrib/imbalanced-learn] it has (to this date):
- 359 Stargazers
- 125 Forks
- 30 Watchers
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Dayvid Victor 2016-07-19 11:33:50 -03:00 committed by GitHub
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@ -757,6 +757,7 @@ on MNIST digits[DEEP LEARNING]
* [Bolt](https://github.com/pprett/bolt) - Bolt Online Learning Toolbox
* [CoverTree](https://github.com/patvarilly/CoverTree) - Python implementation of cover trees, near-drop-in replacement for scipy.spatial.kdtree
* [nilearn](https://github.com/nilearn/nilearn) - Machine learning for NeuroImaging in Python
* [imbalanced-learn](http://contrib.scikit-learn.org/imbalanced-learn/) - Python module to perform under sampling and over sampling with various techniques.
* [Shogun](https://github.com/shogun-toolbox/shogun) - The Shogun Machine Learning Toolbox
* [Pyevolve](https://github.com/perone/Pyevolve) - Genetic algorithm framework.
* [Caffe](http://caffe.berkeleyvision.org) - A deep learning framework developed with cleanliness, readability, and speed in mind.