cla_mlp {daltoolbox}

MLP for classification

Description

Creates a classification object that uses the Multi-Layer Perceptron (MLP) method. It wraps the nnet library.

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

Value

a classification object

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