{ "info": { "author": "Jiang Chen", "author_email": "criver@gmail.com", "bugtrack_url": null, "classifiers": [ "Development Status :: 5 - Production/Stable", "Intended Audience :: Developers", "License :: OSI Approved :: Apache Software License", "Operating System :: MacOS :: MacOS X", "Operating System :: POSIX :: Linux", "Programming Language :: C++", "Programming Language :: Python :: 2", "Programming Language :: Python :: 2.6", "Programming Language :: Python :: 2.7", "Topic :: Scientific/Engineering :: Artificial Intelligence" ], "description": "GBDT is a high performance and full featured C++ implementation of [Jerome H. Friedman's Gradient Boosting Decision Trees Algorithm](http://statweb.stanford.edu/~jhf/ftp/stobst.pdf) and its modern offsprings,. It features high efficiency, low memory footprint, collections of loss functions and built-in mechanisms to handle categorical features and missing values.\n\nWhen is GBDT good for you?\n-----------\n* **You are looking beyond linear models.**\n * Gradient Boosting Decision Trees Algorithms is one of the best offshelf ML algorithms with built-in capabilities of non-linear transformation and feature crossing.\n* **Your data is too big to load into memory with existing ML packages.**\n * GBDT reduces memory footprint dramatically with feature bucketization. For some tested datasets, it used 1/7 of the memory of its counterpart and took only 1/2 time to train. See [docs/PERFORMANCE_BENCHMARK.md](https://github.com/yarny/gbdt/blob/master/docs/PERFORMANCE_BENCHMARK.md) for more details.\n* **You want better handling of categorical features and missing values.**\n * GBDT has built-in mechanisms to figure out how to split categorical features and place missing values in the trees.\n* **You want to try different loss functions.**\n * GBDT implements various pointwise, pairwise, listingwis loss functions including mse, logloss, huberized hinge loss, pairwise logloss,\n[GBRank](http://www.cc.gatech.edu/~zha/papers/fp086-zheng.pdf) and [LambdaMart](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/MSR-TR-2010-82.pdf). 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