MLP: Classification using Multilayer Perceptron

In fdm2id: Data Mining and R Programming for Beginners

Classification using Multilayer Perceptron

Description

This function builds a classification model using Multilayer Perceptron.

Usage

MLP(
  train,
  labels,
  hidden = if (is.vector(train)) 2:(1 + nlevels(labels)) else 2:(ncol(train) +
    nlevels(labels)),
  decay = 10^(-3:-1),
  nfolds = 10,
  tune = FALSE,
  methodparameters = NULL,
  graph = FALSE,
  seed = NULL,
  ...
)

Arguments

train	The training set (description), as a data.frame.
labels	Class labels of the training set (vector or factor).
hidden	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.
graph	Present for interface consistency with performance (which always passes it when fitting a model). Currently unused: MLP 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.

Value

The classification model.

See Also

nnet

Examples

require (datasets)
data (iris)
MLP (iris [, -5], iris [, 5], hidden = 4, decay = .1)

fdm2id documentation built on Aug. 28, 2026, 9:07 a.m.