MachineShop (version 3.3.0)

NNetModel: Neural Network Model

Description

Fit single-hidden-layer neural network, possibly with skip-layer connections.

Usage

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
)

Arguments

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: TRUE, other: FALSE].

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 [-rang, rang].

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 abstol, indicating an essentially perfect fit.

reltol

stop if the optimizer is unable to reduce the fit criterion by a factor of at least 1 - reltol.

Value

MLModel class object.

Details

Response types:

factor, numeric

Automatic tuning of grid parameters:

size, decay

Default values and further model details can be found in the source link below.

See Also

nnet, fit, resample

Examples

# NOT RUN {
fit(sale_amount ~ ., data = ICHomes, model = NNetModel)

# }