In bcjaeger/aorsf: Accelerated Oblique Random Forests

For classification, we fit an oblique RF to predict penguin species using penguin data from the magnificent palmerpenguins R package

# An oblique classification RF
penguin_fit <- orsf(data = penguins_orsf,
                    n_tree = 5, 
                    formula = species ~ .)

penguin_fit

For regression, we use the same data but predict bill length of penguins:

# An oblique regression RF
bill_fit <- orsf(data = penguins_orsf, 
                 n_tree = 5, 
                 formula = bill_length_mm ~ .)

bill_fit

My personal favorite is the oblique survival RF with accelerated Cox regression because it was the first type of oblique RF that aorsf provided (see ArXiv paper; the paper is also published in Journal of Computational and Graphical Statistics but is not publicly available there). Here, we use it to predict mortality risk following diagnosis of primary biliary cirrhosis:

# An oblique survival RF
pbc_fit <- orsf(data = pbc_orsf, 
                n_tree = 5,
                formula = Surv(time, status) ~ . - id)

pbc_fit