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gbm (version 2.1.1)

Generalized Boosted Regression Models

Description

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).

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Version

Install

install.packages('gbm')

Monthly Downloads

30,675

Version

2.1.1

License

GPL (>= 2) | file LICENSE

Maintainer

Harry Southworth

Last Published

March 11th, 2015

Functions in gbm (2.1.1)

pretty.gbm.tree

Print gbm tree components
shrink.gbm

L1 shrinkage of the predictor variables in a GBM
interact.gbm

Estimate the strength of interaction effects
validate.gbm

Test the gbm package.
basehaz.gbm

Baseline hazard function
relative.influence

Methods for estimating relative influence
gbmCrossVal

Cross-validate a gbm
print.gbm

Print model summary
shrink.gbm.pred

Predictions from a shrunked GBM
reconstructGBMdata

Reconstruct a GBM's Source Data
predict.gbm

Predict method for GBM Model Fits
plot.gbm

Marginal plots of fitted gbm objects
gbm.roc.area

Compute Information Retrieval measures.
gbm.perf

GBM performance
summary.gbm

Summary of a gbm object
gbm.object

Generalized Boosted Regression Model Object
gbm-internal

gbm internal functions
calibrate.plot

Calibration plot
quantile.rug

Quantile rug plot
gbm

Generalized Boosted Regression Modeling
gbm-package

Generalized Boosted Regression Models