Fit single-hidden-layer neural network, possibly with skip-layer connections.
NNetModel(
size = 1,
linout = logical(),
entropy = logical(),
softmax = logical(),
censored = FALSE,
skip = FALSE,
rang = 0.7,
decay = 0,
maxit = 100,
trace = FALSE,
MaxNWts = 1000,
abstol = 1e-04,
reltol = 1e-08
)
MLModel
class object.
number of units in the hidden layer.
switch for linear output units. Set automatically according to
the class type of the response variable [numeric: TRUE
, other:
FALSE
].
switch for entropy (= maximum conditional likelihood) fitting.
switch for softmax (log-linear model) and maximum conditional likelihood fitting.
a variant on softmax, in which non-zero targets mean possible classes.
switch to add skip-layer connections from input to output.
Initial random weights on [-rang
, rang
].
parameter for weight decay.
maximum number of iterations.
switch for tracing optimization.
maximum allowable number of weights.
stop if the fit criterion falls below abstol
, indicating
an essentially perfect fit.
stop if the optimizer is unable to reduce the fit criterion by
a factor of at least 1 - reltol
.
factor
, numeric
size
, decay
Default argument values and further model details can be found in the source See Also link below.
nnet
, fit
, resample
fit(sale_amount ~ ., data = ICHomes, model = NNetModel)
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