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

In EnsemblePenReg: Extensible Classes and Methods for Penalized-Regression-Based Integration of Base Learners

Description

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

Usage

epenreg.baselearner.control(
  baselearners = c("nnet","rf","svm","gbm","knn")
  , baselearner.configs = make.configs(baselearners, type = "regression")
  , npart = 1, nfold = 5
)
epenreg.integrator.control(errfun=rmse.error, alpha=1.0
  , n=100, 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"), K-Nearest Neighbors ("knn"), Penalized Rergession ("penreg") and Bayesian Additive Regression Trees ("bart"). The last two learners are not include in the default list: "penreg" tends to produce highly correlated, and generally imprecise, predictions and skews the integration stage towards itself. "bart", on the other hand, is quite time- and memory-consuming to train, depsite generally having superior predictive performance. Users with more CPU and memory resources can add "bart" to achieve higher predictive accuracy.
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.
alpha	Determining L1 vs L2 penalty. alpha=1 leads to Lasso (L1) shrinkage, while alpha=0.0 leads to Ridge (L2) shrinkage. See glmnet help files for more.
n	Suggested number of lambda's in Penalized Regression. Actual number may be smaller than n, and is determined by the glmnet package.
method	Integrator method. Currently, only option is "default", where PenReg is performed on all base learner instances, and CV error is used to find the optimal shrinkage parameter. Same CV-based PenReg 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

EnsemblePenReg documentation built on May 2, 2019, 7:29 a.m.