README.Rmd

---
output: github_document
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  cache = FALSE,
  collapse = TRUE,
  comment = "#>",
  echo = TRUE
)
library(mlr3extralearners)
library(magrittr)
```

# mlr3extralearners

Package website: [release](https://mlr3extralearners.mlr-org.com/) \|
[dev](https://mlr3extralearners.mlr-org.com/dev/)

Extra Learners for **[mlr3](https://github.com/mlr-org/mlr3/)**.

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## What is mlr3extralearners?

`mlr3extralearners` contains all learners from mlr3 that are not in `mlr3learners` or the core packages.
An overview of all learners within the `mlr3verse` can be found [here](https://mlr-org.com/learners.html).

`mlr3extralearners` lives on GitHub and will not be on CRAN.

You can install the package as follows:

```{r, eval = FALSE}
# latest GitHub release
remotes::install_github("mlr-org/mlr3extralearners@*release")

# development version
remotes::install_github("mlr-org/mlr3extralearners")
```



Alternatively, you can add the following to your .Rprofile, which allows you to install `mlr3extralearners` via `install.packages()`.
Note that this will install the development version.

```{r, eval = FALSE}
# .Rprofile
options(repos = c(
  mlrorg = "https://mlr-org.r-universe.dev",
  CRAN = "https://cloud.r-project.org/"
))
```

## Installing and Loading Learners

The package includes functionality for detecting if you have the required packages installed
to use a learner, and ships with the function `install_learner` which can install all required
learner dependencies.

```{r, echo=TRUE, eval = FALSE}
lrn("regr.gbm")
#> Warning: Package 'gbm' required but not installed for Learner 'regr.gbm'
#> <LearnerRegrGBM:regr.gbm>: Gradient Boosting
#> * Model: -
#> * Parameters: keep.data=FALSE, n.cores=1
#> * Packages: mlr3, mlr3extralearners, gbm
#> * Predict Types:  [response]
#> * Feature Types: integer, numeric, factor, ordered
#> * Properties: importance, missings, weights

install_learners("regr.gbm")

lrn("regr.gbm")
#> <LearnerRegrGBM:regr.gbm>: Gradient Boosting
#> * Model: -
#> * Parameters: keep.data=FALSE, n.cores=1
#> * Packages: mlr3, mlr3extralearners, gbm
#> * Predict Types:  [response]
#> * Feature Types: integer, numeric, factor, ordered
#> * Properties: importance, missings, weights
```

## Extending mlr3extralearners

An in-depth tutorial on how to add learners can be found in the [package website](https://mlr3extralearners.mlr-org.com/articles/extending.html).