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gbm (version 2.1.3)

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

2.1.3

License

GPL (>= 2) | file LICENSE

Maintainer

ORPHANED

Last Published

March 21st, 2017

Functions in gbm (2.1.3)

gbm-package

Generalized Boosted Regression Models
shrink.gbm.pred

Predictions from a shrunked GBM
summary.gbm

Summary of a gbm object
relative.influence

Methods for estimating relative influence
shrink.gbm

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

Marginal plots of fitted gbm objects
interact.gbm

Estimate the strength of interaction effects
validate.gbm

Test the gbm package.
gbm.perf

GBM performance
gbm

Generalized Boosted Regression Modeling
gbm-internal

gbm internal functions
gbmCrossVal

Cross-validate a gbm
quantile.rug

Quantile rug plot
gbm.roc.area

Compute Information Retrieval measures.
gbm.object

Generalized Boosted Regression Model Object
basehaz.gbm

Baseline hazard function
calibrate.plot

Calibration plot
reconstructGBMdata

Reconstruct a GBM's Source Data
predict.gbm

Predict method for GBM Model Fits
pretty.gbm.tree

Print gbm tree components
print.gbm

Print model summary