Generalized Boosted Regression Models
Baseline hazard function
Calibration plot
gbm internal functions
Generalized Boosted Regression Models (GBMs)
Generalized Boosted Regression Modeling (GBM)
Generalized Boosted Regression Modeling (GBM)
Generalized Boosted Regression Model Object
GBM performance
Generalized Boosted Regression Modeling (GBM)
Compute Information Retrieval measures.
Cross-validate a gbm
Estimate the strength of interaction effects
Marginal plots of fitted gbm objects
Predict method for GBM Model Fits
Print gbm tree components
Print model summary
Quantile rug plot
Reconstruct a GBM's Source Data
Methods for estimating relative influence
Summary of a gbm object
Test the gbm package.
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. Newer version available at github.com/gbm-developers/gbm3.