cv_dnn: Cross Validate Tuning Parameters of Deep Neural Network

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

Description

Cross Validate Tuning Parameters of Deep Neural Network

Usage

cv_dnn(
  formula,
  data,
  cv.method = "boot632",
  folds = NULL,
  nfolds = 5,
  max.neuron = 10,
  max.layers = 3,
  actfun = "sigm",
  outfun = "linear",
  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".
folds	a vector of pre-set cross-validation or bootstrap folds from caret::createResample or caret::createFolds.
nfolds	the number of bootstrap or cross-validation folds to use. defaults to 5.
max.layers	the largest number of layers to consider.
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 per layer to consider.

Value

a train object

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