bm_gbm: Fit Generalized Boosted Regression models

In tsensembler: Dynamic Ensembles for Time Series Forecasting

Description

Learning a Boosted Tree Model from training data. Parameter setting can vary in interaction.depth, n.trees, and shrinkage parameters.

Usage

bm_gbm(form, data, lpars)

Arguments

form	formula
data	training data for building the predictive model
lpars	a list containing the learning parameters

Details

See gbm for a comprehensive description.

Imports learning procedure from gbm package.

See Also

other learning models: bm_mars; bm_ppr; bm_gaussianprocess; bm_glm; bm_cubist; bm_randomforest; bm_pls_pcr; bm_ffnn; bm_svr

Other base learning models: bm_cubist(), bm_ffnn(), bm_gaussianprocess(), bm_glm(), bm_mars(), bm_pls_pcr(), bm_ppr(), bm_randomforest(), bm_svr()

tsensembler documentation built on Oct. 27, 2020, 5:07 p.m.