{ "info": { "author": "nekoumei", "author_email": "nekoumei@gmail.com", "bugtrack_url": null, "classifiers": [ "Development Status :: 4 - Beta", "Environment :: MacOS X", "License :: OSI Approved :: MIT License", "Programming Language :: Python :: 3.6", "Topic :: Scientific/Engineering :: Artificial Intelligence" ], "description": "# USBaggingClassifier\n# Overview\nBagging Classifier with Under Sampling. \nThis approach is good for classification imbalanced data. \nYou can use both of Binary or Multi-Class Classification. \nMethods could use looks like sci-kit learn's APIs. \nOnly use in python 3.x\n# Usage\n## Parameters\n* base_estimator : object \nClassifier looks like sklearn.XXClassifier. \nClassifier must have methods [fit(X, y), predict(X)]. \nIt is not nesessary predict_proba(X), but if it has this method, \nyou could select 'soft voting' option and get predict probability. \n* n_estimators : int (default=10) \nThe number of base estimators. \n* voting : str {'hard','soft'} 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