CatBoost¶
CatBoost is a machine learning method based on gradient boosting over decision trees.
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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. |
License |
Apache 2.0 |
Project age |
5 years 4 months |
Backers |
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Lastest News (2022-09-26) |
Release 1.1 New features: Multiquantile regression; Support text and embedding features for regression and ranking; Read/write Spark’s … more |
Size score (1 to 10, higher is better) |
9.25 |
Trend score (1 to 10, higher is better) |
5.75 |
Education Resources¶
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Resource Type |
Description |
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Documentation |
Official project documentation. |
Git Commit Statistics¶
Statistics computed using Git data through November 30, 2022.
Statistic |
Lifetime |
Last 12 Months |
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Commits |
111,574 |
28,181 |
Lines committed |
422,173,437 |
62,824,769 |
Unique committers |
955 |
239 |
Core committers |
8 |
6 |
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Trend Score |
Byline |
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3.25 |
5.5 |
A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models in Keras. |
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6.25 |
5.25 |
Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Flink and DataFlow. |