Parallelize 'caret' functions

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The futurize package allows you to easily turn sequential code into parallel code by piping the sequential code to the futurize() function. Easy!

TL;DR

library(futurize)
plan(multisession)
library(caret)

ctrl <- trainControl(method = "cv", number = 10)
model <- train(Species ~ ., data = iris, method = "rf", trControl = ctrl) |> futurize()

Introduction

This vignette demonstrates how to use this approach to parallelize caret functions such as train().

The caret package provides a rich set of machine-learning tools with a unified API. The train() function fits models using cross-validation or bootstrap resampling, making it an excellent candidate for parallelization.

Example: Training a random forest with cross-validation

The train() function fits models across multiple resampling iterations:

library(caret)

## Set up 10-fold cross-validation
ctrl <- trainControl(method = "cv", number = 10)

## Train a random forest model
model <- train(Species ~ ., data = iris, method = "rf", trControl = ctrl)

Here train() evaluates sequentially, but we can easily make it evaluate in parallel by piping to futurize():

library(futurize)
library(caret)

ctrl <- trainControl(method = "cv", number = 10)
model <- train(Species ~ ., data = iris, method = "rf", trControl = ctrl) |> futurize()

This will distribute the cross-validation folds across the available parallel workers, given that we have set up parallel workers, e.g.

plan(multisession)

The built-in multisession backend parallelizes on your local computer and works on all operating systems. There are other parallel backends to choose from, including alternatives to parallelize locally as well as distributed across remote machines, e.g.

plan(future.mirai::mirai_multisession)

and

plan(future.batchtools::batchtools_slurm)

Supported Functions

The following caret functions are supported by futurize():

Henrik Bengtsson

TL;DR

Introduction

Example: Training a random forest with cross-validation

Supported Functions