# mlr3extralearners

**Repository Path**: lg21c/mlr3extralearners

## Basic Information

- **Project Name**: mlr3extralearners
- **Description**: Extra learners for use in mlr3.
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: cubist_mars
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-10-22
- **Last Updated**: 2021-10-22

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README


# 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. `mlr3extralearners` contains helper
functions to find where all the learners, across the mlr3verse, live and
to install required packages to run these learners. See the interactive
[learner
list](https://mlr3extralearners.mlr-org.com/articles/learners/list_learners.html)
for the full list of learners in the mlr3verse and the [learner status
page](https://mlr3extralearners.mlr-org.com/articles/learners/learner_status.html)
for a live build status.

``` r
list_mlr3learners(select = c("id", "mlr3_package", "required_packages"))
#>                      id      mlr3_package      required_packages
#>   1: classif.AdaBoostM1 mlr3extralearners                  RWeka
#>   2:       classif.bart mlr3extralearners                 dbarts
#>   3:        classif.C50 mlr3extralearners                    C50
#>   4:   classif.catboost mlr3extralearners               catboost
#>   5:    classif.cforest mlr3extralearners partykit,sandwich,coin
#>  ---                                                            
#> 128:        surv.ranger      mlr3learners                 ranger
#> 129:         surv.rfsrc mlr3extralearners randomForestSRC,pracma
#> 130:         surv.rpart         mlr3proba  rpart,distr6,survival
#> 131:           surv.svm mlr3extralearners            survivalsvm
#> 132:       surv.xgboost      mlr3learners                xgboost
```

mlr3extralearners lives on GitHub and will not be on CRAN. Install with:

``` r
remotes::install_github("mlr-org/mlr3extralearners")
```

## 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
lrn("regr.gbm")
#> Error: Required packages not installed, please run `install_learners("regr.gbm")`.
```

``` r
install_learners("regr.gbm")
```

``` r
lrn("regr.gbm")
#> <LearnerRegrGBM:regr.gbm>
#> * Model: -
#> * Parameters: keep.data=FALSE, n.cores=1
#> * Packages: gbm
#> * Predict Type: response
#> * Feature types: integer, numeric, factor, ordered
#> * Properties: importance, missings, weights
```

## Extending mlr3extralearners

New learners can be created with the `create_learner` function. This
assumes you have a local copy of `mlr3extralearners`. This function will
automatically create the learner, learner tests, parameter tests, YAML
files for CI if required, and update the DESCRIPTION if required. Once
all tests are passing locally, open a [pull
request](https://github.com/mlr-org/mlr3extralearners/pulls) with the
“New Learner” template.

``` r
create_learner(classname = "Locfit",
               algorithm = "localised fit",
               type = "dens",
               key = "locfit",
               package = "locfit",
               caller = "density.lf",
               feature_types = c("integer", "numeric"),
               predict_types = c("pdf", "cdf"),
               properties = NULL,
               importance = FALSE,
               oob_error = FALSE,
               references = FALSE,
               gh_name = "RaphaelS1")
```