Metadata-Version: 1.1
Name: combo
Version: 0.0.0
Summary: A python toolbox for combining machine learning models
Home-page: https://github.com/yzhao062/combo
Author: Yue Zhao
Author-email: zhaoy@cmu.edu
License: UNKNOWN
Download-URL: https://github.com/yzhao062/combo/archive/master.zip
Description: combo: A python toolbox for combining machine learning models
        =============================================================
        
        **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 <https://en.wikipedia.org/wiki/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 <https://github.com/numba/numba>`_ and `joblib <https://github.com/joblib/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.
Keywords: ensemble learning,model combination,outlier ensembles,data mining,machine learning
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Financial and Insurance Industry
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Information Technology
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
