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A python toolbox for combining machine learning models

Project description

combo is a comprehensive Python model combination toolkit for fusing/aggregating/selecting multiple base ML estimators, under supervised, unsupervised, and semi-supervised scenarios. Model combination is an important task in ensemble learning, but is often beyond the scope of ensemble learning. For instance, simple averaging the results of the same classifiers with multiple runs is deemed as a good way to eliminate the randomness in the classifier for a better stability. Model combination has been widely used in data science competitions and real-world tasks, such as Kaggle.

combo is featured for:

  • Unified APIs, detailed documentation, and interactive examples across various algorithms.
  • Advanced models, including dynamic classifier/ensemble selection.
  • Comprehensive coverage for supervised, unsupervised, and semi-supervised scenarios.
  • Optimized performance with JIT and parallelization when possible, using numba and joblib.

combo will include various model combination frameworks:

  • Simple methods: averaging, maximization, weighted averaging, thresholding
  • Bucket methods: average of maximization, maximization of average
  • Learning methods: stacking (build an additional classifier to learn base estimator weights)
  • Selection methods: dynamic classifier/ensemble selection
  • Other models

Development Status

combo is currently under development as of July 14, 2019. A concrete plan has been laid out and will be implemented in the next few months.

Watch & Star to get the latest update! Also feel free to send me an email (zhaoy@cmu.edu) for suggestions and ideas.

Project details


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