NNetModel: Neural Network Model

In MachineShop: Machine Learning Models and Tools

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.

Details

Response types:

factor, numeric

Automatic tuning of grid parameters:

size, decay

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

Value

MLModel class object.

See Also

nnet, fit, resample

Examples

fit(sale_amount ~ ., data = ICHomes, model = NNetModel)

MachineShop documentation built on Sept. 11, 2024, 6:28 p.m.