daltoolbox (version 1.2.747)

reg_mlp: 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)

Value

returns a object of class reg_mlp

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.

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