Description Usage Arguments Details
Fit a neural network model
1 2 3 4 
formula 
A formula of the form 
data 
A 
subset 
An optional vector specifying a subset of observations to be
used in the fitting process, or, the name of a variable in 
weights 
An optional vector of sampling weights, or the
name of a variable in 
output 
One of 
missing 
How missing data is to be treated. Options:

normalize 
Logical; if 
seed 
The random number seed. 
show.labels 
Shows the variable labels, as opposed to the labels, in the outputs, where a variables label is an attribute (e.g., attr(foo, "label")). 
hidden.nodes 
A 
max.epochs 
Integer; the maximum number of epochs for which to train the network. 
Categorical predictor variables are converted to binary (dummy) variables.
The model is trained first using a random 70
crossvalidation loss on the remaining 30
max.epochs
and 3 epochs of no improvement in crossvalidation loss. The final model
is then retrained on all data (after any "subset"
).
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