# 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()`:

- `bag()`
- `gafs()`
- `nearZeroVar()`
- `rfe()`
- `safs()`
- `sbf()`
- `train()`