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 requires the following dependencies:
- Python (>= 3.8)
- NumPy (>= 1.17.3)
- SciPy (>= 1.3.2)
- Scikit-learn (>= 1.0.2)
Additionally, imbalanced-learn requires the following optional dependencies:
- Pandas (>= 1.0.5) for dealing with dataframes
- Tensorflow (>= 2.4.3) for dealing with TensorFlow models
- Keras (>= 2.4.3) for dealing with Keras models
The examples will requires the following additional dependencies:
- Matplotlib (>= 3.1.2)
- Seaborn (>= 0.9.0)
imbalanced-learn is currently available on the PyPi's repositories and you can install it via pip:
pip install -U imbalanced-learn
The package is release also in Anaconda Cloud platform:
conda install -c conda-forge 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 pip install .
Be aware that you can install in developer mode with:
pip install --no-build-isolation --editable .
If you wish to make pull-requests on GitHub, we advise you to install pre-commit:
pip install pre-commit pre-commit install
After installation, you can use pytest to run the test suite:
make coverage
The development of this scikit-learn-contrib is in line with the one of the scikit-learn community. Therefore, you can refer to their Development Guide.
If you use imbalanced-learn in a scientific publication, we would appreciate citations to the following paper:
@article{JMLR:v18:16-365,
author = {Guillaume Lema{{\^i}}tre and Fernando Nogueira and Christos K. Aridas},
title = {Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning},
journal = {Journal of Machine Learning Research},
year = {2017},
volume = {18},
number = {17},
pages = {1-5},
url = {http://jmlr.org/papers/v18/16-365}
}
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 addressing 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.
You can refer to the imbalanced-learn documentation to find details about the implemented algorithms.