reg_rf: Random Forest for regression

In daltoolbox: Leveraging Experiment Lines to Data Analytics

Random Forest for regression

Description

Regression via Random Forests, an ensemble of decision trees trained on bootstrap samples with random feature subsetting at each split. This wrapper uses the randomForest package API.

Usage

reg_rf(attribute, nodesize = 1, ntree = 10, mtry = NULL)

Arguments

attribute	attribute target to model building
nodesize	node size
ntree	number of trees
mtry	number of attributes to build tree

Details

Random Forests reduce variance and are robust to overfitting on tabular data. Key hyperparameters are the number of trees (ntree), the number of variables tried at each split (mtry), and the minimum node size (nodesize).

Value

returns an object of class reg_rfobj

References

Breiman, L. (2001). Random Forests. Machine Learning 45(1):5–32. Liaw, A. and Wiener, M. (2002). Classification and Regression by randomForest. R News.

Examples

data(Boston)
model <- reg_rf("medv", ntree=10)

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

model <- fit(model, train)

test_prediction <- predict(model, test)
test_predictand <- test[,"medv"]
test_eval <- evaluate(model, test_predictand, test_prediction)
test_eval$metrics

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