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catboost/catboost

Repository: catboost/catboost Stars: 9079 Forks: 1327 Primary language: C++ Discovery sources: awesome:awesome-data-science, awesome:awesome-python Selection score: 83.17 Usefulness score: 9.7 Source confidence score: 5.1 Languages: C++, Python, Cuda, CMake, Scala, Cython Topics: big-data, catboost, categorical-features, coreml, cuda, data-mining, data-science, decision-trees, gbdt, gbm Summary: A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU. README excerpt: <img src=http://storage.mds.yandex.net/get-devtools-opensource/250854/catboost-logo.png width=300/> [Website](https://catboost.ai) | [Documentation](https://catboost.ai/docs/) | [Tutorials](https://catboost.ai/docs/concepts/tutorials.html) | [Installation](https://catboost.ai/docs/concepts/installation.html) | [Release Notes](https://github.com/catboost/catboost/releases) [](https://github.com/catboost/catboost/blob/master/LICENSE) [](https://badge.fury.io/py/catboost) [](https://anaconda.org/conda-forge/catboost) [](https://github.com/catboost/catboost/issues) [](https://t.me/catboost_en) [](https://twitter.com/CatBoostML) CatBoost is a machine learning method based on [gradient boosting](https://en.wikipedia.org/wiki/Gradient_boosting) over decision trees. Main advantages of CatBoost: -------------- - Superior quality [compared](https://github.com/catboost/benchmarks/blob/master/README.md) with other GBDT libraries on many datasets. - Best-in-class [prediction](https://catboost.ai/docs/concepts/c-plus-plus-api.html) speed. - Support for both [numerical and categorical](https://catboost.ai/docs/concepts/algorithm-main-stages.html) features. -

Computing & Technology, Computer Science, Machine Learning