reg_mlp: MLP for regression

In daltoolbox: Leveraging Experiment Lines to Data Analytics

MLP for regression

Description

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

Usage

reg_mlp(attribute, size = NULL, decay = 0.05, maxit = 1000)

Arguments

attribute	attribute target to model building
size	number of neurons in hidden layers
decay	decay learning rate
maxit	number of maximum iterations for training

Details

Feedforward neural network with size hidden units and L2 regularization controlled by decay. Data should be scaled for stable training.

Value

returns a object of class reg_mlp

References

Bishop, C. M. (1995). Neural Networks for Pattern Recognition. Oxford University Press.

Examples

data(Boston)
model <- reg_mlp("medv", size=5, decay=0.54)

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

model <- fit(model, train)

test_prediction <- predict(model, test)
test_predictand <- test[,"medv"]
test_eval <- evaluate(model, test_predictand, test_prediction)
test_eval$metrics

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