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