README.Rmd

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# gbm

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## Overview

`gbm` (which stands for **g**eneralized **b**oosted **m**odels) implements extensions to Freund and Schapire's AdaBoost algorithm and [Friedman's gradient boosting machine](http://projecteuclid.org/euclid.aos/1013203451). It 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 (i.e., LambdaMart).

**Note:** This is a maintained version of `gbm` back compatible to CRAN versions of `gbm` 2.1.x. It exists mainly for the purpose of reproducible research and data analyses performed with the 2.1.x versions of `gbm`. For newer development, and a more consistent API, try out the newer [gbm3](https://github.com/gbm-developers/gbm3) package!