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gbm (version 2.0-5)

Generalized Boosted Regression Models

Description

This package implements 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).

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Version

Install

install.packages('gbm')

Monthly Downloads

25,231

Version

2.0-5

License

GPL (>= 2)

Maintainer

Greg Ridgeway

Last Published

December 8th, 2012

Functions in gbm (2.0-5)

gbm-package

Generalized Boosted Regression Models
gbm.roc.area

Compute Information Retrieval measures.
gbm.object

Generalized Boosted Regression Model Object
interact.gbm

Estimate the strength of interaction effects
reconstructGBMdata

Reconstruct a GBM's Source Data
summary.gbm

Summary of a gbm object
basehaz.gbm

Baseline hazard function
predict.gbm

Predict method for GBM Model Fits
quantile.rug

Quantile rug plot
print.gbm

Print model summary
relative.influence

Methods for estimating relative influence
gbm.perf

GBM performance
shrink.gbm.pred

Predictions from a shrunked GBM
pretty.gbm.tree

Print gbm tree components
validate.gbm

Test the gbm package.
shrink.gbm

L1 shrinkage of the predictor variables in a GBM
gbm

Generalized Boosted Regression Modeling
plot.gbm

Marginal plots of fitted gbm objects
calibrate.plot

Calibration plot