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gbm (version 1.3-3)

Generalized Boosted Regression Models

Description

This package implements extensions to Freund and Schapire's AdaBoost algorithm and J. Friedman's gradient boosting machine. Includes regression methods for least squares, absolute loss, logistic, Poisson, Cox proportional hazards partial likelihood, and AdaBoost exponential loss.

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Version

Install

install.packages('gbm')

Monthly Downloads

25,231

Version

1.3-3

License

GPL (version 2 or newer)

Maintainer

Greg Ridgeway

Last Published

June 28th, 2024

Functions in gbm (1.3-3)

plot.gbm

Marginal plots of fitted gbm objects
shrink.gbm.pred

Predictions from a shrunked GBM
predict.gbm

Predict method for GBM Model Fits
gbm.object

Generalized Boosted Regression Model Object
shrink.gbm

L1 shrinkage of the predictor variables in a GBM
calibrate.plot

Calibration plot
gbm.perf

GBM performance
quantile.rug

Quantile rug plot
gbm

Generalized Boosted Regression Modeling
pretty.gbm.tree

Print gbm tree components
relative.influence

Methods for estimating relative influence
summary.gbm

Summary of a gbm object