cla_mlp: MLP for classification

In daltoolbox: Leveraging Experiment Lines to Data Analytics

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)

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.

Value

returns a classification object

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

daltoolbox documentation built on Nov. 5, 2025, 7:09 p.m.