cla_mlp: MLP for classification

In daltoolbox: Leveraging Experiment Lines to Data Analytics

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

returns 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

daltoolbox documentation built on Nov. 3, 2024, 9:06 a.m.