cv_nnet: Cross Validate Number of Neurons in Neural Network

In abnormally-distributed/cvreg: Cross Validation and Robust Estimation Utilities

Description

Cross Validate Number of Neurons in Neural Network

Usage

cv_nnet(
  formula,
  data,
  cv.method = "boot632",
  nfolds = 5,
  max.neuron = 10,
  folds = NULL,
  nrep = 4,
  crit = "MAE",
  select = "oneSE"
)

Arguments

formula	a model formula
data	a training data set
cv.method	preferably one of "boot632" (the default), "cv", or "repeatedcv".
nfolds	the number of bootstrap or cross-validation folds to use. defaults to 5.
folds	a vector of pre-set cross-validation or bootstrap folds from caret::createResample or caret::createFolds.
nrep	the number of repetitions for cv.method = "repeatedcv". defaults to 4.
crit	the criterion by which to evaluate the model performance. must be one of "MAE" (the default) or "MSE".
select	the selection rule to use. Should be one of "best" or "oneSE" (the default).
max.neurons	the largest number of neurons to consider.

Value

a train object

abnormally-distributed/cvreg documentation built on May 3, 2020, 3:45 p.m.