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depscope/conda/r-gbm

r-gbm

condav2.2.3

An implementation of extensions to Freund and Schapire's AdaBoost algorithm and Friedman's gradient boosting machine. Includes regression methods for least squares, absolute loss, t-distribution loss, quantile regression, logistic, multinomial logistic, Poisson, Cox proportional hazards partial likelihood, AdaBoost exponential loss, Huberized hinge loss, and Learning to Rank measures (LambdaMart). Originally developed by Greg Ridgeway.

License GPL-2.0-or-later8 versions1 maintainers0 deps596 weekly dl
57
/ 100
Health
safe to use

[email protected] is safe to use (health: 57/100)

Health breakdown0 – 100
15/25
maintenance
3/20
popularity
25/25
security
12/15
maturity
2/15
community
Vulnerabilities
0
none known

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First published · 2020-07-15 10:44:53.912000+00:00

Last updated · 2026-01-24 14:04:25.049000+00:00

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