daltoolbox (version 1.2.747)

cla_dtree: Decision Tree for classification

Description

Univariate decision tree for classification using recursive partitioning. This wrapper uses the tree package.

Usage

cla_dtree(attribute, slevels)

Value

returns a classification object

Arguments

attribute

attribute target to model building

slevels

the possible values for the target classification

Details

Decision trees split the feature space by maximizing node purity (e.g., Gini/entropy), yielding a human‑readable set of rules. They are fast and interpretable, and often used as base learners in ensembles.

References

Breiman, L., Friedman, J., Olshen, R., and Stone, C. (1984). Classification and Regression Trees. Wadsworth.

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