daltoolbox (version 1.2.747)

reg_rf: 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)

Value

returns an object of class reg_rfobj

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).

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