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https://github.com/mlr-org/mlr3learners
Recommended learners for mlr3
https://github.com/mlr-org/mlr3learners
classification learners machine-learning mlr3 r r-package regression
Last synced: 7 days ago
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Recommended learners for mlr3
- Host: GitHub
- URL: https://github.com/mlr-org/mlr3learners
- Owner: mlr-org
- License: lgpl-3.0
- Created: 2018-10-05T20:44:02.000Z (about 6 years ago)
- Default Branch: main
- Last Pushed: 2024-09-09T06:30:53.000Z (3 months ago)
- Last Synced: 2024-09-29T10:09:13.667Z (3 months ago)
- Topics: classification, learners, machine-learning, mlr3, r, r-package, regression
- Language: R
- Homepage: https://mlr3learners.mlr-org.com
- Size: 11.4 MB
- Stars: 89
- Watchers: 18
- Forks: 14
- Open Issues: 15
-
Metadata Files:
- Readme: README.md
- Changelog: NEWS.md
- License: LICENSE
Awesome Lists containing this project
README
# mlr3learners
Package website: [release](https://mlr3learners.mlr-org.com/) | [dev](https://mlr3learners.mlr-org.com/dev/)
[](https://github.com/mlr-org/mlr3learners/actions/workflows/r-cmd-check.yml)
[](https://github.com/mlr-org/mlr3learners/actions)
[](https://cran.r-project.org/package=mlr3learners)
[](https://stackoverflow.com/questions/tagged/mlr3)
[](https://lmmisld-lmu-stats-slds.srv.mwn.de/mlr_invite/)
This packages provides essential learners for [mlr3](https://mlr3.mlr-org.com), maintained by the mlr-org team.
Additional learners can be found in the [mlr3extralearners](https://github.com/mlr-org/mlr3extralearners) package on GitHub.
Request additional learners over there.
:point_right: [Table of all learners](https://mlr-org.com/learners.html)
## Installation
```r
# CRAN version:
install.packages("mlr3learners")
# Development version:
remotes::install_github("mlr-org/mlr3learners")
```
If you also want to install all packages of the connected learners, set `dependencies = TRUE`:
```r
# CRAN version:
install.packages("mlr3learners", dependencies = TRUE)
# Development version:
remotes::install_github("mlr-org/mlr3learners", dependencies = TRUE)
```
## Classification Learners
| ID | Learner | Package |
| :------------------------------------------------------------------------------------------------------ | :---------------------------- | :---------------------------------------------------- |
| [classif.cv_glmnet](https://mlr3learners.mlr-org.com/reference/mlr_learners_classif.cv_glmnet.html) | Penalized Logistic Regression | [glmnet](https://cran.r-project.org/package=glmnet) |
| [classif.glmnet](https://mlr3learners.mlr-org.com/reference/mlr_learners_classif.glmnet.html) | Penalized Logistic Regression | [glmnet](https://cran.r-project.org/package=glmnet) |
| [classif.kknn](https://mlr3learners.mlr-org.com/reference/mlr_learners_classif.kknn.html) | k-Nearest Neighbors | [kknn](https://cran.r-project.org/package=kknn) |
| [classif.lda](https://mlr3learners.mlr-org.com/reference/mlr_learners_classif.lda.html) | LDA | [MASS](https://cran.r-project.org/package=MASS) |
| [classif.log_reg](https://mlr3learners.mlr-org.com/reference/mlr_learners_classif.log_reg.html) | Logistic Regression | stats |
| [classif.multinom](https://mlr3learners.mlr-org.com/reference/mlr_learners_classif.multinom.html) | Multinomial log-linear model | [nnet](https://cran.r-project.org/package=nnet) |
| [classif.naive_bayes](https://mlr3learners.mlr-org.com/reference/mlr_learners_classif.naive_bayes.html) | Naive Bayes | [e1071](https://cran.r-project.org/package=e1071) |
| [classif.nnet](https://mlr3learners.mlr-org.com/reference/mlr_learners_classif.nnet.html) | Single Layer Neural Network | [nnet](https://cran.r-project.org/package=nnet) |
| [classif.qda](https://mlr3learners.mlr-org.com/reference/mlr_learners_classif.qda.html) | QDA | [MASS](https://cran.r-project.org/package=MASS) |
| [classif.ranger](https://mlr3learners.mlr-org.com/reference/mlr_learners_classif.ranger.html) | Random Forest | [ranger](https://cran.r-project.org/package=ranger) |
| [classif.svm](https://mlr3learners.mlr-org.com/reference/mlr_learners_classif.svm.html) | SVM | [e1071](https://cran.r-project.org/package=e1071) |
| [classif.xgboost](https://mlr3learners.mlr-org.com/reference/mlr_learners_classif.xgboost.html) | Gradient Boosting | [xgboost](https://cran.r-project.org/package=xgboost) |
## Regression Learners
| ID | Learner | Package |
| :-------------------------------------------------------------------------------------------- | :-------------------------- | :------------------------------------------------------------ |
| [regr.cv_glmnet](https://mlr3learners.mlr-org.com/reference/mlr_learners_regr.cv_glmnet.html) | Penalized Linear Regression | [glmnet](https://cran.r-project.org/package=glmnet) |
| [regr.glmnet](https://mlr3learners.mlr-org.com/reference/mlr_learners_regr.glmnet.html) | Penalized Linear Regression | [glmnet](https://cran.r-project.org/package=glmnet) |
| [regr.kknn](https://mlr3learners.mlr-org.com/reference/mlr_learners_regr.kknn.html) | k-Nearest Neighbors | [kknn](https://cran.r-project.org/package=kknn) |
| [regr.km](https://mlr3learners.mlr-org.com/reference/mlr_learners_regr.km.html) | Kriging | [DiceKriging](https://cran.r-project.org/package=DiceKriging) |
| [regr.lm](https://mlr3learners.mlr-org.com/reference/mlr_learners_regr.lm.html) | Linear Regression | stats |
| [regr.nnet](https://mlr3learners.mlr-org.com/reference/mlr_learners_regr.nnet.html) | Single Layer Neural Network | nnet |
| [regr.ranger](https://mlr3learners.mlr-org.com/reference/mlr_learners_regr.ranger.html) | Random Forest | [ranger](https://cran.r-project.org/package=ranger) |
| [regr.svm](https://mlr3learners.mlr-org.com/reference/mlr_learners_regr.svm.html) | SVM | [e1071](https://cran.r-project.org/package=e1071) |
| [regr.xgboost](https://mlr3learners.mlr-org.com/reference/mlr_learners_regr.xgboost.html) | Gradient Boosting | [xgboost](https://cran.r-project.org/package=xgboost) |