Toolbox for imbalanced dataset in machine learning.
imbalanced-learn is a python package offering a number of re-sampling techniques commonly used in datasets showing strong between-class imbalance. It is compatible with scikit-learn and is part of scikit-learn-contrib projects.
Installation documentation, API documentation, and examples can be found on the documentation.
imbalanced-learn is tested to work under Python 2.7 and Python 3.5.
imbalanced-learn is currently available on the PyPi’s reporitories and you can install it via pip:
pip install -U imbalanced-learn
The package is release also in Anaconda Cloud platform:
conda install -c glemaitre imbalanced-learn
If you prefer, you can clone it and run the setup.py file. Use the following commands to get a copy from Github and install all dependencies:
git clone https://github.com/scikit-learn-contrib/imbalanced-learn.git cd imbalanced-learn python setup.py install
After installation, you can use nose to run the test suite:
Most classification algorithms will only perform optimally when the number of samples of each class is roughly the same. Highly skewed datasets, where the minority is heavily outnumbered by one or more classes, have proven to be a challenge while at the same time becoming more and more common.
One way of addresing this issue is by re-sampling the dataset as to offset this imbalance with the hope of arriving at a more robust and fair decision boundary than you would otherwise.
- Re-sampling techniques are divided in two categories:
Under-sampling the majority class(es).
Over-sampling the minority class.
Combining over- and under-sampling.
Create ensemble balanced sets.
Below is a list of the methods currently implemented in this module.
Random majority under-sampling with replacement
Extraction of majority-minority Tomek links 
Under-sampling with Cluster Centroids
NearMiss-(1 & 2 & 3) 
Condensend Nearest Neighbour 
One-Sided Selection 
Neighboorhood Cleaning Rule 
Edited Nearest Neighbours 
Instance Hardness Threshold 
Repeated Edited Nearest Neighbours 
The different algorithms are presented in the following notebook.
This is a work in progress. Any comments, suggestions or corrections are welcome.
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