cla_dtree: Decision Tree for classification

In daltoolbox: Leveraging Experiment Lines to Data Analytics

Decision Tree for classification

Description

Creates a classification object that uses the Decision Tree algorithm for classification. It wraps the tree library.

Usage

cla_dtree(attribute, slevels)

Arguments

attribute	attribute target to model building
slevels	the possible values for the target classification

Value

returns a classification object

Examples

data(iris)
slevels <- levels(iris$Species)
model <- cla_dtree("Species", slevels)

# 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.