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 ```r 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: ```r 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()`: ```r 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. ```r 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. ```r plan(future.mirai::mirai_multisession) ``` and ```r plan(future.batchtools::batchtools_slurm) ``` # Supported Functions The following **caret** functions are supported by `futurize()`: * `bag()` * `gafs()` * `nearZeroVar()` * `rfe()` * `safs()` * `sbf()` * `train()` [caret]: https://cran.r-project.org/package=caret