Metadata-Version: 1.1
Name: gbdt
Version: 0.3
Summary: High performance implementation of GBDT family of algorithm
Home-page: https://github.com/yarny/gbdt
Author: Jiang Chen
Author-email: criver@gmail.com
License: Apache 2.0
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.
        
        When is GBDT good for you?
        -----------
        * **You are looking beyond linear models.**
          * Gradient Boosting Decision Trees Algorithms is one of the best offshelf ML algorithms with built-in capabilities of non-linear transformation and feature crossing.
        * **Your data is too big to load into memory with existing ML packages.**
          * 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.
        * **You want better handling of categorical features and missing values.**
          * GBDT has built-in mechanisms to figure out how to split categorical features and place missing values in the trees.
        * **You want to try different loss functions.**
          * GBDT implements various pointwise, pairwise, listingwis loss functions including mse, logloss, huberized hinge loss, pairwise logloss,
        [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). It supports easily addition of your own custom loss functions.
            
Keywords: gbdt,machine learning,decision trees,forest
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: POSIX :: Linux
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: C++
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.6
Classifier: Programming Language :: Python :: 2.7
