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).
|Author||Greg Ridgeway <[email protected]> with contributions from others|
|Date of publication||2017-03-21 06:48:03 UTC|
|License||GPL (>= 2) | file LICENSE|
|Package repository||View on CRAN|
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