cla_dtree: Decision Tree for classification

In daltoolbox: Leveraging Experiment Lines to Data Analytics

Decision Tree for classification

Description

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

Usage

cla_dtree(attribute, slevels)

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.

Value

returns a classification object

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

daltoolbox documentation built on Nov. 5, 2025, 7:09 p.m.