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gbm

Generalized Boosted Regression Models

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). Originally developed by Greg Ridgeway.

Functions (21)

basehaz.gbm

Baseline hazard function

calibrate.plot

Calibration plot

gbm

Generalized Boosted Regression Modeling (GBM)

gbm-internals

gbm internal functions

gbm-package

Generalized Boosted Regression Models (GBMs)

gbm.fit

Generalized Boosted Regression Modeling (GBM)

gbm.more

Generalized Boosted Regression Modeling (GBM)

gbm.object

Generalized Boosted Regression Model Object

gbm.perf

GBM performance

gbm.roc.area

Compute Information Retrieval measures.

gbmCrossVal

Cross-validate a gbm

interact.gbm

Estimate the strength of interaction effects

plot.gbm

Marginal plots of fitted gbm objects

predict.gbm

Predict method for GBM Model Fits

pretty.gbm.tree

Print gbm tree components

print.gbm

Print model summary

quantile.rug

Quantile rug plot

reconstructGBMdata

Reconstruct a GBM's Source Data

relative.influence

Methods for estimating relative influence

summary.gbm

Summary of a gbm object

test.gbm

Test the gbm package.

gbm

Generalized Boosted Regression Models

v2.1.8

GPL (>= 2) | file LICENSE

Authors

Brandon Greenwell [aut, cre] (<https://orcid.org/0000-0002-8120-0084>), Bradley Boehmke [aut] (<https://orcid.org/0000-0002-3611-8516>), Jay Cunningham [aut], GBM Developers [aut] (https://github.com/gbm-developers)

Initial release

gbm

Functions (21)

gbm

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