Generalized Boosted Models: A guide to the gbm package

Boosting takes on various forms with different programs using different loss functions, different base models, and different optimization schemes. The gbm package takes the approach described in [2] and [3]. Some of the terminology differs, mostly due to an effort to cast boosting terms into more standard sta-tistical terminology (e.g. deviance). In addition, the gbm package implements boosting for models commonly used in statistics but not commonly associated with boosting. The Cox proportional hazard model, for example, is an incred-ibly useful model and the boosting framework applies quite readily with only slight modification [5]. Also some algorithms implemented in the gbm package differ from the standard…