cla_tune: Classification tuning (k-fold CV)

In daltoolbox: Leveraging Experiment Lines to Data Analytics

Classification tuning (k-fold CV)

Description

Tune hyperparameters of a base classifier via k‑fold cross‑validation using a chosen metric.

Usage

cla_tune(base_model, folds = 10, ranges = NULL, metric = "accuracy")

Arguments

base_model	base model for tuning
folds	number of folds for cross-validation
ranges	a list of hyperparameter ranges to explore
metric	metric used to optimize

Value

returns a cla_tune object

References

Kohavi, R. (1995). A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection.

Examples

# preparing dataset for random sampling
sr <- sample_random()
sr <- train_test(sr, iris)
train <- sr$train
test <- sr$test

# hyper parameter setup
tune <- cla_tune(cla_mlp("Species", levels(iris$Species)),
  ranges=list(size=c(3:5), decay=c(0.1)))

# hyper parameter optimization
model <- fit(tune, train)

# testing optimization
test_prediction <- predict(model, test)
test_predictand <- adjust_class_label(test[,"Species"])
test_eval <- evaluate(model, test_predictand, test_prediction)
test_eval$metrics

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