epcreg.baselearner.control: Utility Functions for Configuring Regression Base Learners...

In EnsemblePCReg: Extensible Package for Principal-Component-Regression-Based Heterogeneous Ensemble Meta-Learning

Utility Functions for Configuring Regression Base Learners and Integrator in EnsemblePCReg Package

Description

Function epcreg.baselearner.control sets up the base learners used in the epcreg call. Function epcreg.integrator.control sets up the PCR integrator.

Usage

epcreg.baselearner.control(
  baselearners = c("nnet","rf","svm","gbm","knn")
  , baselearner.configs = make.configs(baselearners, type = "regression")
  , npart = 1, nfold = 5
)
epcreg.integrator.control(errfun=rmse.error, nfold=5, method=c("default"))

Arguments

baselearners	Names of base learners used. Currently, regression options available are Neural Network ("nnet"), Random Forest ("rf"), Support Vector Machine ("svm"), Gradient Boosting Machine ("gbm"), and K-Nearest Neighbors ("knn").
baselearner.configs	List of base learner configurations. Default is to call make.configs from package EnsembleBase.
npart	Number of partitions to train each base learner configuration in a CV scheme.
nfold	Number of folds within each data partition.
errfun	Error function used to compare performance of base learner configurations. Default is to use rmse.error from package EnsembleBase.
method	Integrator method. Currently, only option is "default", where PCR is performed on all base learner instances, and CV error is used to find the optimal number of PC's. Same CV-based PCR output is used to make final prediction.

Value

Both functions return lists with same element names as function arguments.

Author(s)

Mansour T.A. Sharabiani, Alireza S. Mahani

See Also

make.configs, rmse.error

EnsemblePCReg documentation built on April 19, 2022, 1:06 a.m.