daltoolbox (version 1.2.747)

cla_mlp: MLP for classification

Description

Multi-Layer Perceptron classifier using nnet::nnet (single hidden layer).

Usage

cla_mlp(attribute, slevels, size = NULL, decay = 0.1, maxit = 1000)

Value

returns a classification object

Arguments

attribute

attribute target to model building

slevels

possible values for the target classification

size

number of nodes that will be used in the hidden layer

decay

how quickly it decreases in gradient descent

maxit

maximum iterations

Details

Uses softmax output with one‑hot targets from adjust_class_label. size controls hidden units and decay applies L2 regularization. Features should be scaled.

References

Rumelhart, D., Hinton, G., Williams, R. (1986). Learning representations by back‑propagating errors. Bishop, C. M. (1995). Neural Networks for Pattern Recognition.

Examples

data(iris)
slevels <- levels(iris$Species)
model <- cla_mlp("Species", slevels, size=3, decay=0.03)

# preparing dataset for random sampling
sr <- sample_random()
sr <- train_test(sr, iris)
train <- sr$train
test <- sr$test

model <- fit(model, train)

prediction <- predict(model, test)
predictand <- adjust_class_label(test[,"Species"])
test_eval <- evaluate(model, predictand, prediction)
test_eval$metrics