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imbalanced-learn is a python package offering a number of re-sampling techniquescommonly used in datasets showing strong between-class imbalance.It is compatible withscikit-learn and is part ofscikit-learn-contribprojects.

Installation documentation, API documentation, and examples can be found on thedocumentation.

imbalanced-learn requires the following dependencies:

• Python (>= 3.8)
• NumPy (>= 1.17.3)
• SciPy (>= 1.3.2)
• Scikit-learn (>= 1.1.0)

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 caninstall 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 followingcommands to get a copy from Github and install all dependencies:

git clone https://github.com/scikit-learn-contrib/imbalanced-learn.gitcd imbalanced-learnpip 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 installpre-commit:

pip install pre-commitpre-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 oneof the scikit-learn community. Therefore, you can refer to theirDevelopment Guide.

If you use imbalanced-learn in a scientific publication, we would appreciatecitations 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 ofsamples of each class is roughly the same. Highly skewed datasets, where theminority is heavily outnumbered by one or more classes, have proven to be achallenge 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 thisimbalance with the hope of arriving at a more robust and fair decision boundarythan you would otherwise.

Re-sampling techniques are divided in two categories:

1. Under-sampling the majority class(es).
2. Over-sampling the minority class.
3. Combining over- and under-sampling.
4. Create ensemble balanced sets.

Below is a list of the methods currently implemented in this module.

• Under-sampling

  1. Random majority under-sampling with replacement
  2. Extraction of majority-minority Tomek links[1]
  3. Under-sampling with Cluster Centroids
  4. NearMiss-(1 & 2 & 3)[2]
  5. Condensed Nearest Neighbour[3]
  6. One-Sided Selection[4]
  7. Neighboorhood Cleaning Rule[5]
  8. Edited Nearest Neighbours[6]
  9. Instance Hardness Threshold[7]
  10. Repeated Edited Nearest Neighbours[14]
  11. AllKNN[14]

• Over-sampling

  1. Random minority over-sampling with replacement
  2. SMOTE - Synthetic Minority Over-sampling Technique[8]
  3. SMOTENC - SMOTE for Nominal and Continuous[8]
  4. SMOTEN - SMOTE for Nominal[8]
  5. bSMOTE(1 & 2) - Borderline SMOTE of types 1 and 2[9]
  6. SVM SMOTE - Support Vectors SMOTE[10]
  7. ADASYN - Adaptive synthetic sampling approach for imbalanced learning[15]
  8. KMeans-SMOTE[17]
  9. ROSE - Random OverSampling Examples[19]

• Over-sampling followed by under-sampling

  1. SMOTE + Tomek links[12]
  2. SMOTE + ENN[11]

• Ensemble classifier using samplers internally

  1. Easy Ensemble classifier[13]
  2. Balanced Random Forest[16]
  3. Balanced Bagging
  4. RUSBoost[18]

• Mini-batch resampling for Keras and Tensorflow

The different algorithms are presented in thesphinx-gallery.