Description Usage Arguments Value Author(s) See Also
Function ecv.regression.baselearner.control
sets up the base learners used in the ecv.regression
call.
1 2 3 4 5 6 | 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"))
|
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 |
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", which uses average error for each base learner configuration across repeated CV runs to chose the best configuration. |
Both functions return lists with same element names as function arguments.
Mansour T.A. Sharabiani, Alireza S. Mahani
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.