gbm - Generalized Boosted Regression Models
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. Newer version available at github.com/gbm-developers/gbm3.
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cpp
14.14 score 54 stars 98 dependents 8.3k scripts 35k downloadsgbm3 - Generalized Boosted Regression Models
Extensions to Freund and Schapire's AdaBoost algorithm, Y. Freund and R. Schapire (1997) <doi:10.1006/jcss.1997.1504> and Friedman's gradient boosting machine, J.H. Friedman (2001) <doi:10.1214/aos/1013203451>. Includes regression methods for least squares, absolute loss, t-distribution loss, quantile regression, logistic, Poisson, Cox proportional hazards partial likelihood, AdaBoost exponential loss, Huberized hinge loss, and Learning to Rank measures (LambdaMART).
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cppopenmp
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