ecv.regression.baselearner.control {EnsembleCV}

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

Description

Function ecv.regression.baselearner.control sets up the base learners used in the ecv.regression call.

Usage

ecv.regression.baselearner.control(
  baselearners = c("nnet", "rf", "svm", "gbm", "knn", "penreg")
  , baselearner.configs = make.configs(baselearners, type = "regression")
  , npart = 1, nfold = 5
)
ecv.regression.integrator.control(errfun=rmse.error, 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"), Penalized Rergession ("penreg") and Bayesian Additive Regression Trees ("bart"). The last learner is not included by default, due to significantly longer training time needed by it ("bart") compared to other learners.

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", which uses average error for each base learner configuration across repeated CV runs to chose the best configuration.

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