epcreg.baselearner.control | R Documentation |
Function epcreg.baselearner.control
sets up the base learners used in the epcreg
call. Function epcreg.integrator.control
sets up the PCR integrator.
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"))
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 |
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 |
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. |
Both functions return lists with same element names as function arguments.
Mansour T.A. Sharabiani, Alireza S. Mahani
make.configs
, rmse.error
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