fdm2id (version 1.0.1)

MLPREG: Multi-Layer Perceptron Regression

Description

This function builds a regression model using MLP.

Usage

MLPREG(
  x,
  y,
  size = if (is.vector(x)) 2 else 2:ncol(x),
  decay = 10^(-3:-1),
  nfolds = 10,
  tune = FALSE,
  methodparameters = NULL,
  graph = FALSE,
  seed = NULL,
  ...
)

Value

The classification model, as an object of class model-class.

Arguments

x

Predictor matrix.

y

Response vector.

size

The size of the hidden layer (if a vector, cross-over validation is used to chose the best size).

decay

The decay (between 0 and 1) of the backpropagation algorithm (if a vector, cross-over validation is used to chose the best size).

nfolds

The number of folds of the cross-validation a method runs to choose its hyperparameters. Only used when there is something to choose, i.e. when one of them is given as a vector. Lower it to fit faster, at the cost of a noisier choice.

tune

If true, the function returns parameters instead of a classification model.

methodparameters

Object containing the parameters. If given, it replaces size and decay. Named (and behaves identically to) methodparameters rather than params, for consistency with MLP and with the calling convention used by performance/the internal protocol.* functions, which always pass a methodparameters argument.

graph

Present for interface consistency with performance (which always passes it when fitting a model). Currently unused: MLPREG does not produce a plot.

seed

A specified seed for random number generation, so that two runs on the same data give the same model. Every learning method accepts it, so that it can be set the same way whatever the method; the deterministic ones simply have nothing to draw and give the same model with or without it.

...

Other parameters.

See Also

nnet

Examples

if (FALSE) {
require (datasets)
data (trees)
MLPREG (trees [, -3], trees [, 3])
}