Cross-validation for a Neural Network

Cross-validation for a Neural Network

cv.nn(
  object,
  K = 5,
  repeats = 1,
  decay = seq(0, 1, 0.2),
  size = 1:5,
  seed = 1234,
  trace = TRUE,
  fun,
  ...
)

Arguments

object	Object of type "nn" or "nnet"
K	Number of cross validation passes to use
repeats	Repeated cross validation
decay	Parameter decay
size	Number of units (nodes) in the hidden layer
seed	Random seed to use as the starting point
trace	Print progress
fun	Function to use for model evaluation (i.e., auc for classification and RMSE for regression)
...	Additional arguments to be passed to 'fun'

Value

A data.frame sorted by the mean of the performance metric

Details

See https://radiant-rstats.github.io/docs/model/nn.html for an example in Radiant

See also

nn to generate an initial model that can be passed to cv.nn

Rsq to calculate an R-squared measure for a regression

RMSE to calculate the Root Mean Squared Error for a regression

MAE to calculate the Mean Absolute Error for a regression

auc to calculate the area under the ROC curve for classification

profit to calculate profits for classification at a cost/margin threshold

Examples

if (FALSE) {
result <- nn(dvd, "buy", c("coupon", "purch", "last"))
cv.nn(result, decay = seq(0, 1, .5), size = 1:2)
cv.nn(result, decay = seq(0, 1, .5), size = 1:2, fun = profit, cost = 1, margin = 5)
result <- nn(diamonds, "price", c("carat", "color", "clarity"), type = "regression")
cv.nn(result, decay = seq(0, 1, .5), size = 1:2)
cv.nn(result, decay = seq(0, 1, .5), size = 1:2, fun = Rsq)
}