mlr3learners v0.1.5

Recommended Learners for 'mlr3'

Recommended Learners for 'mlr3'. Extends 'mlr3' with interfaces to essential machine learning packages on CRAN. This includes, but is not limited to: (penalized) linear and logistic regression, linear and quadratic discriminant analysis, k-nearest neighbors, naive Bayes, support vector machines, and gradient boosting.

Readme

mlr3learners

This packages provides essential learners for mlr3, maintained by the mlr-org team. We will most likely not add new learners to this package.

Other learners live in the mlr3learners organization and are possibly maintained by people outside the mlr-org team. There is a wiki page listing all currently available custom learners. A guide on how to create custom learners is covered in the mlr3book. Feel invited to contribute a missing learner to the mlr3 ecosystem :-)

Classification Learners

ID	Learner	Package
classif.glmnet	Penalized Logistic Regression	glmnet
classif.kknn	k-Nearest Neighbors	kknn
classif.lda	LDA	MASS
classif.log_reg	Logistic Regression	stats
classif.naive_bayes	Naive Bayes	e1071
classif.qda	QDA	MASS
classif.ranger	Random Forest	ranger
classif.svm	SVM	e1071
classif.xgboost	Gradient Boosting	xgboost

Regression Learners

ID	Learner	Package
regr.glmnet	Penalized Linear Regression	glmnet
regr.kknn	k-Nearest Neighbors	kknn
regr.km	Kriging	DiceKriging
regr.lm	Linear Regression	stats
regr.ranger	Random Forest	ranger
regr.svm	SVM	e1071
regr.xgboost	Gradient Boosting	xgboost

Functions in mlr3learners

Name	Description
LearnerRegrGlmnet	GLM with Elastic Net Regularization Regression Learner
LearnerClassifXgboost	Extreme Gradient Boosting Classification Learner
mlr3learners-package	mlr3learners: Recommended Learners for 'mlr3'
LearnerClassifLDA	Linear Discriminant Analysis Classification Learner
LearnerClassifLogReg	Logistic Regression Classification Learner
LearnerRegrKKNN	k-Nearest-Neighbor Regression Learner
LearnerClassifSVM	Support Vector Machine
LearnerClassifRanger	Ranger Classification Learner
LearnerRegrSVM	Support Vector Machine
LearnerClassifQDA	Quadratic Discriminant Analysis Classification Learner
LearnerRegrKM	Kriging Regression Learner
LearnerRegrLM	Linear Model Regression Learner
LearnerClassifKKNN	k-Nearest-Neighbor Classification Learner
LearnerClassifGlmnet	GLM with Elastic Net Regularization Classification Learner
LearnerRegrXgboost	Extreme Gradient Boosting Regression Learner
LearnerRegrRanger	Ranger Regression Learner
LearnerClassifNaiveBayes	Naive Bayes Classification Learner
No Results!

Name

Description

LearnerRegrGlmnet

GLM with Elastic Net Regularization Regression Learner

LearnerClassifXgboost

Extreme Gradient Boosting Classification Learner

mlr3learners-package

mlr3learners: Recommended Learners for 'mlr3'

LearnerClassifLDA

Linear Discriminant Analysis Classification Learner

LearnerClassifLogReg

Logistic Regression Classification Learner

LearnerRegrKKNN

k-Nearest-Neighbor Regression Learner

LearnerClassifSVM

Support Vector Machine

LearnerClassifRanger

Ranger Classification Learner

LearnerRegrSVM

Support Vector Machine

LearnerClassifQDA

Quadratic Discriminant Analysis Classification Learner

LearnerRegrKM

Kriging Regression Learner

LearnerRegrLM

Linear Model Regression Learner

LearnerClassifKKNN

k-Nearest-Neighbor Classification Learner

LearnerClassifGlmnet

GLM with Elastic Net Regularization Classification Learner

LearnerRegrXgboost

Extreme Gradient Boosting Regression Learner

LearnerRegrRanger

Ranger Regression Learner

LearnerClassifNaiveBayes

Naive Bayes Classification Learner

No Results!

Details

License	LGPL-3
URL	https://mlr3learners.mlr-org.com, https://github.com/mlr-org/mlr3learners
BugReports	https://github.com/mlr-org/mlr3learners/issues
RdMacros	mlr3misc
Encoding	UTF-8
NeedsCompilation	no
RoxygenNote	7.0.0
Packaged	2019-11-25 14:21:27 UTC; lang
Repository	CRAN
Date/Publication	2019-11-25 15:00:05 UTC

suggests	bibtex , checkmate , DiceKriging , e1071 , glmnet , kknn , lgr , MASS , ranger , testthat , withr , xgboost
imports	data.table , mlr3 (>= 0.1.4) , mlr3misc (>= 0.1.5) , paradox , R6
depends	R (>= 3.1.0)
Contributors	Patrick Schratz, Stefan Coors, Quay Au