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General purpose gradient boosting on decision trees library with categorical features support out of the box. It is easy to install, contains fast inference implementation and supports CPU and GPU (even multi-GPU) computation.

Uploaded Mon Mar 31 20:58:51 2025
md5 checksum e304d275e2f93d438731b3481b43d74d
arch x86_64
build py310h06a4308_1
build_number 1
depends matplotlib-base, numpy >=1.16.0,<2.0a0, pandas >=0.24.0, plotly, python >=3.10,<3.11.0a0, python-graphviz, scipy, six
license Apache-2.0
license_family MIT
md5 e304d275e2f93d438731b3481b43d74d
name catboost
platform linux
sha1 419e265110ee9a6959a50d03f3ec21d23c90be3d
sha256 b85292e3267ce41d568147d7030ab0ffe5564c7c84a427b8fcde9687b83f8364
size 34546776
subdir linux-64
timestamp 1659346493976
version 1.0.6