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 = ifelse(is.vector(train), 2:(1 + nlevels(labels)), 2:(ncol(train) +
    nlevels(labels))),
  decay = 10^(-3:-1),
  methodparameters = NULL,
  tune = FALSE,
  ...
)

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).
methodparameters	Object containing the parameters. If given, it replaces size and decay.
tune	If true, the function returns paramters instead of a classification model.
...	Other parameters.

Value

The classification model.

See Also

nnet

Examples

## Not run: 
require (datasets)
data (iris)
MLP (iris [, -5], iris [, 5], hidden = 4, decay = .1)

## End(Not run)

fdm2id documentation built on July 9, 2023, 6:05 p.m.