cv.nn {radiant.model} | R Documentation |
Cross-validation for a Neural Network
Description
Cross-validation for a Neural Network
Usage
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' |
Details
See https://radiant-rstats.github.io/docs/model/nn.html for an example in Radiant
Value
A data.frame sorted by the mean of the performance metric
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
## Not run:
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)
## End(Not run)