Title:	Recommended Learners for 'mlr3'
Version:	0.12.0
Description:	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.
License:	LGPL-3
URL:	https://mlr3learners.mlr-org.com, https://github.com/mlr-org/mlr3learners
BugReports:	https://github.com/mlr-org/mlr3learners/issues
Depends:	mlr3 (≥ 0.23.0), R (≥ 3.1.0)
Imports:	checkmate, data.table, mlr3misc (≥ 0.9.4), paradox (≥ 1.0.0), R6
Suggests:	DiceKriging, e1071, glmnet, kknn, knitr, lgr, MASS, nnet, pracma, ranger, rgenoud, rmarkdown, testthat (≥ 3.0.0), xgboost (≥ 1.6.0)
Config/testthat/edition:	3
Encoding:	UTF-8
NeedsCompilation:	no
RoxygenNote:	7.3.2
Collate:	'aaa.R' 'LearnerClassifCVGlmnet.R' 'LearnerClassifGlmnet.R' 'LearnerClassifKKNN.R' 'LearnerClassifLDA.R' 'LearnerClassifLogReg.R' 'LearnerClassifMultinom.R' 'LearnerClassifNaiveBayes.R' 'LearnerClassifNnet.R' 'LearnerClassifQDA.R' 'LearnerClassifRanger.R' 'LearnerClassifSVM.R' 'LearnerClassifXgboost.R' 'LearnerRegrCVGlmnet.R' 'LearnerRegrGlmnet.R' 'LearnerRegrKKNN.R' 'LearnerRegrKM.R' 'LearnerRegrLM.R' 'LearnerRegrNnet.R' 'LearnerRegrRanger.R' 'LearnerRegrSVM.R' 'LearnerRegrXgboost.R' 'bibentries.R' 'helpers.R' 'helpers_glmnet.R' 'helpers_ranger.R' 'zzz.R'
Packaged:	2025-05-21 07:14:41 UTC; marc
Author:	Michel Lang [aut], Quay Au [aut], Stefan Coors [aut], Patrick Schratz [aut], Marc Becker [cre, aut], John Zobolas [aut]
Maintainer:	Marc Becker <marcbecker@posteo.de>
Repository:	CRAN
Date/Publication:	2025-05-23 08:42:03 UTC

mlr3learners: Recommended Learners for 'mlr3'

Description

More learners are implemented in the mlr3extralearners package. A guide on how to create custom learners is covered in the book: https://mlr3book.mlr-org.com. Feel invited to contribute a missing learner to the mlr3 ecosystem!

Author(s)

Maintainer: Marc Becker marcbecker@posteo.de (ORCID)

Authors:

• Michel Lang michellang@gmail.com (ORCID)

• Quay Au quayau@gmail.com (ORCID)

• Stefan Coors mail@stefancoors.de (ORCID)

• Patrick Schratz patrick.schratz@gmail.com (ORCID)

• John Zobolas bblodfon@gmail.com (ORCID)

See Also

Useful links:

• https://mlr3learners.mlr-org.com

• https://github.com/mlr-org/mlr3learners

• Report bugs at https://github.com/mlr-org/mlr3learners/issues

GLM with Elastic Net Regularization Classification Learner

Description

Generalized linear models with elastic net regularization. Calls glmnet::cv.glmnet() from package glmnet.

The default for hyperparameter family is set to "binomial" or "multinomial", depending on the number of classes.

Offset

If a Task contains a column with the offset role, it is automatically incorporated during training via the offset argument in glmnet::glmnet(). During prediction, the offset column from the test set is used only if use_pred_offset = TRUE (default), passed via the newoffset argument in glmnet::predict.glmnet(). Otherwise, if the user sets use_pred_offset = FALSE, a zero offset is applied, effectively disabling the offset adjustment during prediction.

Dictionary

This mlr3::Learner can be instantiated via the dictionary mlr3::mlr_learners or with the associated sugar function mlr3::lrn():

mlr_learners$get("classif.cv_glmnet")
lrn("classif.cv_glmnet")

Meta Information

• Task type: “classif”

• Predict Types: “response”, “prob”

• Feature Types: “logical”, “integer”, “numeric”

• Required Packages: mlr3, mlr3learners, glmnet

Parameters

Internal Encoding

Starting with mlr3 v0.5.0, the order of class labels is reversed prior to model fitting to comply to the stats::glm() convention that the negative class is provided as the first factor level.

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifCVGlmnet

Methods

Public methods

• LearnerClassifCVGlmnet$new()

• LearnerClassifCVGlmnet$selected_features()

• LearnerClassifCVGlmnet$clone()

Method new()

Creates a new instance of this R6 class.

Usage

LearnerClassifCVGlmnet$new()

Method selected_features()

Returns the set of selected features as reported by glmnet::predict.glmnet() with type set to "nonzero".

Usage

LearnerClassifCVGlmnet$selected_features(lambda = NULL)

Arguments

Returns

(character()) of feature names.

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifCVGlmnet$clone(deep = FALSE)

Arguments

References

Friedman J, Hastie T, Tibshirani R (2010). “Regularization Paths for Generalized Linear Models via Coordinate Descent.” Journal of Statistical Software, 33(1), 1–22. doi:10.18637/jss.v033.i01.

See Also

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/chapters/chapter2/data_and_basic_modeling.html#sec-learners

• Package mlr3extralearners for more learners.

• Dictionary of Learners: mlr3::mlr_learners

• as.data.table(mlr_learners) for a table of available Learners in the running session (depending on the loaded packages).

• mlr3pipelines to combine learners with pre- and postprocessing steps.

• Extension packages for additional task types:

  • mlr3proba for probabilistic supervised regression and survival analysis.

  • mlr3cluster for unsupervised clustering.

• mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.

Other Learner: mlr_learners_classif.glmnet, mlr_learners_classif.kknn, mlr_learners_classif.lda, mlr_learners_classif.log_reg, mlr_learners_classif.multinom, mlr_learners_classif.naive_bayes, mlr_learners_classif.nnet, mlr_learners_classif.qda, mlr_learners_classif.ranger, mlr_learners_classif.svm, mlr_learners_classif.xgboost, mlr_learners_regr.cv_glmnet, mlr_learners_regr.glmnet, mlr_learners_regr.kknn, mlr_learners_regr.km, mlr_learners_regr.lm, mlr_learners_regr.nnet, mlr_learners_regr.ranger, mlr_learners_regr.svm, mlr_learners_regr.xgboost

Examples

if (requireNamespace("glmnet", quietly = TRUE)) {
# Define the Learner and set parameter values
learner = lrn("classif.cv_glmnet")
print(learner)

# Define a Task
task = tsk("sonar")

# Create train and test set
ids = partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

# print the model
print(learner$model)

# importance method
if("importance" %in% learner$properties) print(learner$importance)

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()
}

GLM with Elastic Net Regularization Classification Learner

Description

Generalized linear models with elastic net regularization. Calls glmnet::glmnet() from package glmnet.

Details

Caution: This learner is different to learners calling glmnet::cv.glmnet() in that it does not use the internal optimization of parameter lambda. Instead, lambda needs to be tuned by the user (e.g., via mlr3tuning). When lambda is tuned, the glmnet will be trained for each tuning iteration. While fitting the whole path of lambdas would be more efficient, as is done by default in glmnet::glmnet(), tuning/selecting the parameter at prediction time (using parameter s) is currently not supported in mlr3 (at least not in efficient manner). Tuning the s parameter is, therefore, currently discouraged.

When the data are i.i.d. and efficiency is key, we recommend using the respective auto-tuning counterparts in mlr_learners_classif.cv_glmnet() or mlr_learners_regr.cv_glmnet(). However, in some situations this is not applicable, usually when data are imbalanced or not i.i.d. (longitudinal, time-series) and tuning requires custom resampling strategies (blocked design, stratification).

Dictionary

This mlr3::Learner can be instantiated via the dictionary mlr3::mlr_learners or with the associated sugar function mlr3::lrn():

mlr_learners$get("classif.glmnet")
lrn("classif.glmnet")

Meta Information

• Task type: “classif”

• Predict Types: “response”, “prob”

• Feature Types: “logical”, “integer”, “numeric”

• Required Packages: mlr3, mlr3learners, glmnet

Parameters

Internal Encoding

Starting with mlr3 v0.5.0, the order of class labels is reversed prior to model fitting to comply to the stats::glm() convention that the negative class is provided as the first factor level.

Offset

If a Task contains a column with the offset role, it is automatically incorporated during training via the offset argument in glmnet::glmnet(). During prediction, the offset column from the test set is used only if use_pred_offset = TRUE (default), passed via the newoffset argument in glmnet::predict.glmnet(). Otherwise, if the user sets use_pred_offset = FALSE, a zero offset is applied, effectively disabling the offset adjustment during prediction.

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifGlmnet

Methods

Public methods

• LearnerClassifGlmnet$new()

• LearnerClassifGlmnet$selected_features()

• LearnerClassifGlmnet$clone()

Method new()

Creates a new instance of this R6 class.

Usage

LearnerClassifGlmnet$new()

Method selected_features()

Returns the set of selected features as reported by glmnet::predict.glmnet() with type set to "nonzero".

Usage

LearnerClassifGlmnet$selected_features(lambda = NULL)

Arguments

Returns

(character()) of feature names.

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifGlmnet$clone(deep = FALSE)

Arguments

References

Friedman J, Hastie T, Tibshirani R (2010). “Regularization Paths for Generalized Linear Models via Coordinate Descent.” Journal of Statistical Software, 33(1), 1–22. doi:10.18637/jss.v033.i01.

See Also

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/chapters/chapter2/data_and_basic_modeling.html#sec-learners

• Package mlr3extralearners for more learners.

• Dictionary of Learners: mlr3::mlr_learners

• as.data.table(mlr_learners) for a table of available Learners in the running session (depending on the loaded packages).

• mlr3pipelines to combine learners with pre- and postprocessing steps.

• Extension packages for additional task types:

  • mlr3proba for probabilistic supervised regression and survival analysis.

  • mlr3cluster for unsupervised clustering.

• mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.

Other Learner: mlr_learners_classif.cv_glmnet, mlr_learners_classif.kknn, mlr_learners_classif.lda, mlr_learners_classif.log_reg, mlr_learners_classif.multinom, mlr_learners_classif.naive_bayes, mlr_learners_classif.nnet, mlr_learners_classif.qda, mlr_learners_classif.ranger, mlr_learners_classif.svm, mlr_learners_classif.xgboost, mlr_learners_regr.cv_glmnet, mlr_learners_regr.glmnet, mlr_learners_regr.kknn, mlr_learners_regr.km, mlr_learners_regr.lm, mlr_learners_regr.nnet, mlr_learners_regr.ranger, mlr_learners_regr.svm, mlr_learners_regr.xgboost

Examples

if (requireNamespace("glmnet", quietly = TRUE)) {
# Define the Learner and set parameter values
learner = lrn("classif.glmnet")
print(learner)

# Define a Task
task = tsk("sonar")

# Create train and test set
ids = partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

# print the model
print(learner$model)

# importance method
if("importance" %in% learner$properties) print(learner$importance)

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()
}

k-Nearest-Neighbor Classification Learner

Description

k-Nearest-Neighbor classification. Calls kknn::kknn() from package kknn.

Initial parameter values

• store_model:

  • See note.

Dictionary

This mlr3::Learner can be instantiated via the dictionary mlr3::mlr_learners or with the associated sugar function mlr3::lrn():

mlr_learners$get("classif.kknn")
lrn("classif.kknn")

Meta Information

• Task type: “classif”

• Predict Types: “response”, “prob”

• Feature Types: “logical”, “integer”, “numeric”, “factor”, “ordered”

• Required Packages: mlr3, mlr3learners, kknn

Parameters

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifKKNN

Methods

Public methods

• LearnerClassifKKNN$new()

• LearnerClassifKKNN$clone()

Method new()

Creates a new instance of this R6 class.

Usage

LearnerClassifKKNN$new()

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifKKNN$clone(deep = FALSE)

Arguments

Note

There is no training step for k-NN models, just storing the training data to process it during the predict step. Therefore, ⁠$model⁠ returns a list with the following elements:

• formula: Formula for calling kknn::kknn() during ⁠$predict()⁠.

• data: Training data for calling kknn::kknn() during ⁠$predict()⁠.

• pv: Training parameters for calling kknn::kknn() during ⁠$predict()⁠.

• kknn: Model as returned by kknn::kknn(), only available after ⁠$predict()⁠ has been called. This is not stored by default, you must set hyperparameter store_model to TRUE.

References

Hechenbichler, Klaus, Schliep, Klaus (2004). “Weighted k-nearest-neighbor techniques and ordinal classification.” Technical Report Discussion Paper 399, SFB 386, Ludwig-Maximilians University Munich. doi:10.5282/ubm/epub.1769.

Samworth, J R (2012). “Optimal weighted nearest neighbour classifiers.” The Annals of Statistics, 40(5), 2733–2763. doi:10.1214/12-AOS1049.

Cover, Thomas, Hart, Peter (1967). “Nearest neighbor pattern classification.” IEEE transactions on information theory, 13(1), 21–27. doi:10.1109/TIT.1967.1053964.

See Also

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/chapters/chapter2/data_and_basic_modeling.html#sec-learners

• Package mlr3extralearners for more learners.

• Dictionary of Learners: mlr3::mlr_learners

• as.data.table(mlr_learners) for a table of available Learners in the running session (depending on the loaded packages).

• mlr3pipelines to combine learners with pre- and postprocessing steps.

• Extension packages for additional task types:

  • mlr3proba for probabilistic supervised regression and survival analysis.

  • mlr3cluster for unsupervised clustering.

• mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.

Other Learner: mlr_learners_classif.cv_glmnet, mlr_learners_classif.glmnet, mlr_learners_classif.lda, mlr_learners_classif.log_reg, mlr_learners_classif.multinom, mlr_learners_classif.naive_bayes, mlr_learners_classif.nnet, mlr_learners_classif.qda, mlr_learners_classif.ranger, mlr_learners_classif.svm, mlr_learners_classif.xgboost, mlr_learners_regr.cv_glmnet, mlr_learners_regr.glmnet, mlr_learners_regr.kknn, mlr_learners_regr.km, mlr_learners_regr.lm, mlr_learners_regr.nnet, mlr_learners_regr.ranger, mlr_learners_regr.svm, mlr_learners_regr.xgboost

Examples

if (requireNamespace("kknn", quietly = TRUE)) {
# Define the Learner and set parameter values
learner = lrn("classif.kknn")
print(learner)

# Define a Task
task = tsk("sonar")

# Create train and test set
ids = partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

# print the model
print(learner$model)

# importance method
if("importance" %in% learner$properties) print(learner$importance)

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()
}

Linear Discriminant Analysis Classification Learner

Description

Linear discriminant analysis. Calls MASS::lda() from package MASS.

Details

Parameters method and prior exist for training and prediction but accept different values for each. Therefore, arguments for the predict stage have been renamed to predict.method and predict.prior, respectively.

Dictionary

This mlr3::Learner can be instantiated via the dictionary mlr3::mlr_learners or with the associated sugar function mlr3::lrn():

mlr_learners$get("classif.lda")
lrn("classif.lda")

Meta Information

• Task type: “classif”

• Predict Types: “response”, “prob”

• Feature Types: “logical”, “integer”, “numeric”, “factor”, “ordered”

• Required Packages: mlr3, mlr3learners, MASS

Parameters

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifLDA

Methods

Public methods

• LearnerClassifLDA$new()

• LearnerClassifLDA$clone()

Method new()

Creates a new instance of this R6 class.

Usage

LearnerClassifLDA$new()

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifLDA$clone(deep = FALSE)

Arguments

References

Venables WN, Ripley BD (2002). Modern Applied Statistics with S, Fourth edition. Springer, New York. ISBN 0-387-95457-0, http://www.stats.ox.ac.uk/pub/MASS4/.

See Also

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/chapters/chapter2/data_and_basic_modeling.html#sec-learners

• Package mlr3extralearners for more learners.

• Dictionary of Learners: mlr3::mlr_learners

• as.data.table(mlr_learners) for a table of available Learners in the running session (depending on the loaded packages).

• mlr3pipelines to combine learners with pre- and postprocessing steps.

• Extension packages for additional task types:

  • mlr3proba for probabilistic supervised regression and survival analysis.

  • mlr3cluster for unsupervised clustering.

• mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.

Other Learner: mlr_learners_classif.cv_glmnet, mlr_learners_classif.glmnet, mlr_learners_classif.kknn, mlr_learners_classif.log_reg, mlr_learners_classif.multinom, mlr_learners_classif.naive_bayes, mlr_learners_classif.nnet, mlr_learners_classif.qda, mlr_learners_classif.ranger, mlr_learners_classif.svm, mlr_learners_classif.xgboost, mlr_learners_regr.cv_glmnet, mlr_learners_regr.glmnet, mlr_learners_regr.kknn, mlr_learners_regr.km, mlr_learners_regr.lm, mlr_learners_regr.nnet, mlr_learners_regr.ranger, mlr_learners_regr.svm, mlr_learners_regr.xgboost

Examples

if (requireNamespace("MASS", quietly = TRUE)) {
# Define the Learner and set parameter values
learner = lrn("classif.lda")
print(learner)

# Define a Task
task = tsk("sonar")

# Create train and test set
ids = partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

# print the model
print(learner$model)

# importance method
if("importance" %in% learner$properties) print(learner$importance)

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()
}

Logistic Regression Classification Learner

Description

Classification via logistic regression. Calls stats::glm() with family set to "binomial".

Internal Encoding

Starting with mlr3 v0.5.0, the order of class labels is reversed prior to model fitting to comply to the stats::glm() convention that the negative class is provided as the first factor level.

Initial parameter values

• model:

  • Actual default: TRUE.

  • Adjusted default: FALSE.

  • Reason for change: Save some memory.

Offset

If a Task has a column with the role offset, it will automatically be used during training. The offset is incorporated through the formula interface to ensure compatibility with stats::glm(). We add it to the model formula as ⁠offset(<column_name>)⁠ and also include it in the training data. During prediction, the default behavior is to use the offset column from the test set (enabled by use_pred_offset = TRUE). Otherwise, if the user sets use_pred_offset = FALSE, a zero offset is applied, effectively disabling the offset adjustment during prediction.

Dictionary

This mlr3::Learner can be instantiated via the dictionary mlr3::mlr_learners or with the associated sugar function mlr3::lrn():

mlr_learners$get("classif.log_reg")
lrn("classif.log_reg")

Meta Information

• Task type: “classif”

• Predict Types: “response”, “prob”

• Feature Types: “logical”, “integer”, “numeric”, “character”, “factor”, “ordered”

• Required Packages: mlr3, mlr3learners, 'stats'

Parameters

Contrasts

To ensure reproducibility, this learner always uses the default contrasts:

• contr.treatment() for unordered factors, and

• contr.poly() for ordered factors.

Setting the option "contrasts" does not have any effect. Instead, set the respective hyperparameter or use mlr3pipelines to create dummy features.

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifLogReg

Methods

Public methods

• LearnerClassifLogReg$new()

• LearnerClassifLogReg$clone()

Method new()

Creates a new instance of this R6 class.

Usage

LearnerClassifLogReg$new()

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifLogReg$clone(deep = FALSE)

Arguments

See Also

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/chapters/chapter2/data_and_basic_modeling.html#sec-learners

• Package mlr3extralearners for more learners.

• Dictionary of Learners: mlr3::mlr_learners

• as.data.table(mlr_learners) for a table of available Learners in the running session (depending on the loaded packages).

• mlr3pipelines to combine learners with pre- and postprocessing steps.

• Extension packages for additional task types:

  • mlr3proba for probabilistic supervised regression and survival analysis.

  • mlr3cluster for unsupervised clustering.

• mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.

Other Learner: mlr_learners_classif.cv_glmnet, mlr_learners_classif.glmnet, mlr_learners_classif.kknn, mlr_learners_classif.lda, mlr_learners_classif.multinom, mlr_learners_classif.naive_bayes, mlr_learners_classif.nnet, mlr_learners_classif.qda, mlr_learners_classif.ranger, mlr_learners_classif.svm, mlr_learners_classif.xgboost, mlr_learners_regr.cv_glmnet, mlr_learners_regr.glmnet, mlr_learners_regr.kknn, mlr_learners_regr.km, mlr_learners_regr.lm, mlr_learners_regr.nnet, mlr_learners_regr.ranger, mlr_learners_regr.svm, mlr_learners_regr.xgboost

Examples

if (requireNamespace("stats", quietly = TRUE)) {
# Define the Learner and set parameter values
learner = lrn("classif.log_reg")
print(learner)

# Define a Task
task = tsk("sonar")

# Create train and test set
ids = partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

# print the model
print(learner$model)

# importance method
if("importance" %in% learner$properties) print(learner$importance)

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()
}

Multinomial log-linear learner via neural networks

Description

Multinomial log-linear models via neural networks. Calls nnet::multinom() from package nnet.

Dictionary

This mlr3::Learner can be instantiated via the dictionary mlr3::mlr_learners or with the associated sugar function mlr3::lrn():

mlr_learners$get("classif.multinom")
lrn("classif.multinom")

Meta Information

• Task type: “classif”

• Predict Types: “response”, “prob”

• Feature Types: “logical”, “integer”, “numeric”, “factor”

• Required Packages: mlr3, mlr3learners, nnet

Parameters

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifMultinom

Methods

Public methods

• LearnerClassifMultinom$new()

• LearnerClassifMultinom$clone()

Method new()

Creates a new instance of this R6 class.

Usage

LearnerClassifMultinom$new()

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifMultinom$clone(deep = FALSE)

Arguments

See Also

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/chapters/chapter2/data_and_basic_modeling.html#sec-learners

• Package mlr3extralearners for more learners.

• Dictionary of Learners: mlr3::mlr_learners

• as.data.table(mlr_learners) for a table of available Learners in the running session (depending on the loaded packages).

• mlr3pipelines to combine learners with pre- and postprocessing steps.

• Extension packages for additional task types:

  • mlr3proba for probabilistic supervised regression and survival analysis.

  • mlr3cluster for unsupervised clustering.

• mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.

Other Learner: mlr_learners_classif.cv_glmnet, mlr_learners_classif.glmnet, mlr_learners_classif.kknn, mlr_learners_classif.lda, mlr_learners_classif.log_reg, mlr_learners_classif.naive_bayes, mlr_learners_classif.nnet, mlr_learners_classif.qda, mlr_learners_classif.ranger, mlr_learners_classif.svm, mlr_learners_classif.xgboost, mlr_learners_regr.cv_glmnet, mlr_learners_regr.glmnet, mlr_learners_regr.kknn, mlr_learners_regr.km, mlr_learners_regr.lm, mlr_learners_regr.nnet, mlr_learners_regr.ranger, mlr_learners_regr.svm, mlr_learners_regr.xgboost

Examples

if (requireNamespace("nnet", quietly = TRUE)) {
# Define the Learner and set parameter values
learner = lrn("classif.multinom")
print(learner)

# Define a Task
task = tsk("sonar")

# Create train and test set
ids = partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

# print the model
print(learner$model)

# importance method
if("importance" %in% learner$properties) print(learner$importance)

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()
}

Naive Bayes Classification Learner

Description

Naive Bayes classification. Calls e1071::naiveBayes() from package e1071.

Dictionary

This mlr3::Learner can be instantiated via the dictionary mlr3::mlr_learners or with the associated sugar function mlr3::lrn():

mlr_learners$get("classif.naive_bayes")
lrn("classif.naive_bayes")

Meta Information

• Task type: “classif”

• Predict Types: “response”, “prob”

• Feature Types: “logical”, “integer”, “numeric”, “factor”

• Required Packages: mlr3, mlr3learners, e1071

Parameters

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifNaiveBayes

Methods

Public methods

• LearnerClassifNaiveBayes$new()

• LearnerClassifNaiveBayes$clone()

Method new()

Creates a new instance of this R6 class.

Usage

LearnerClassifNaiveBayes$new()

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifNaiveBayes$clone(deep = FALSE)

Arguments

See Also

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/chapters/chapter2/data_and_basic_modeling.html#sec-learners

• Package mlr3extralearners for more learners.

• Dictionary of Learners: mlr3::mlr_learners

• as.data.table(mlr_learners) for a table of available Learners in the running session (depending on the loaded packages).

• mlr3pipelines to combine learners with pre- and postprocessing steps.

• Extension packages for additional task types:

  • mlr3proba for probabilistic supervised regression and survival analysis.

  • mlr3cluster for unsupervised clustering.

• mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.

Other Learner: mlr_learners_classif.cv_glmnet, mlr_learners_classif.glmnet, mlr_learners_classif.kknn, mlr_learners_classif.lda, mlr_learners_classif.log_reg, mlr_learners_classif.multinom, mlr_learners_classif.nnet, mlr_learners_classif.qda, mlr_learners_classif.ranger, mlr_learners_classif.svm, mlr_learners_classif.xgboost, mlr_learners_regr.cv_glmnet, mlr_learners_regr.glmnet, mlr_learners_regr.kknn, mlr_learners_regr.km, mlr_learners_regr.lm, mlr_learners_regr.nnet, mlr_learners_regr.ranger, mlr_learners_regr.svm, mlr_learners_regr.xgboost

Examples

if (requireNamespace("e1071", quietly = TRUE)) {
# Define the Learner and set parameter values
learner = lrn("classif.naive_bayes")
print(learner)

# Define a Task
task = tsk("sonar")

# Create train and test set
ids = partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

# print the model
print(learner$model)

# importance method
if("importance" %in% learner$properties) print(learner$importance)

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()
}

Classification Neural Network Learner

Description

Single Layer Neural Network. Calls nnet::nnet.formula() from package nnet.

Note that modern neural networks with multiple layers are connected via package mlr3torch.

Dictionary

This mlr3::Learner can be instantiated via the dictionary mlr3::mlr_learners or with the associated sugar function mlr3::lrn():

mlr_learners$get("classif.nnet")
lrn("classif.nnet")

Meta Information

• Task type: “classif”

• Predict Types: “response”, “prob”

• Feature Types: “logical”, “integer”, “numeric”, “factor”, “ordered”

• Required Packages: mlr3, mlr3learners, nnet

Parameters

Initial parameter values

• size:

  • Adjusted default: 3L.

  • Reason for change: no default in nnet().

Custom mlr3 parameters

• formula: if not provided, the formula is set to task$formula().

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifNnet

Methods

Public methods

• LearnerClassifNnet$new()

• LearnerClassifNnet$clone()

Method new()

Creates a new instance of this R6 class.

Usage

LearnerClassifNnet$new()

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifNnet$clone(deep = FALSE)

Arguments

References

Ripley BD (1996). Pattern Recognition and Neural Networks. Cambridge University Press. doi:10.1017/cbo9780511812651.

See Also

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/chapters/chapter2/data_and_basic_modeling.html#sec-learners

• Package mlr3extralearners for more learners.

• Dictionary of Learners: mlr3::mlr_learners

• as.data.table(mlr_learners) for a table of available Learners in the running session (depending on the loaded packages).

• mlr3pipelines to combine learners with pre- and postprocessing steps.

• Extension packages for additional task types:

  • mlr3proba for probabilistic supervised regression and survival analysis.

  • mlr3cluster for unsupervised clustering.

• mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.

Other Learner: mlr_learners_classif.cv_glmnet, mlr_learners_classif.glmnet, mlr_learners_classif.kknn, mlr_learners_classif.lda, mlr_learners_classif.log_reg, mlr_learners_classif.multinom, mlr_learners_classif.naive_bayes, mlr_learners_classif.qda, mlr_learners_classif.ranger, mlr_learners_classif.svm, mlr_learners_classif.xgboost, mlr_learners_regr.cv_glmnet, mlr_learners_regr.glmnet, mlr_learners_regr.kknn, mlr_learners_regr.km, mlr_learners_regr.lm, mlr_learners_regr.nnet, mlr_learners_regr.ranger, mlr_learners_regr.svm, mlr_learners_regr.xgboost

Examples

if (requireNamespace("nnet", quietly = TRUE)) {
# Define the Learner and set parameter values
learner = lrn("classif.nnet")
print(learner)

# Define a Task
task = tsk("sonar")

# Create train and test set
ids = partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

# print the model
print(learner$model)

# importance method
if("importance" %in% learner$properties) print(learner$importance)

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()
}

Quadratic Discriminant Analysis Classification Learner

Description

Quadratic discriminant analysis. Calls MASS::qda() from package MASS.

Details

Parameters method and prior exist for training and prediction but accept different values for each. Therefore, arguments for the predict stage have been renamed to predict.method and predict.prior, respectively.

Dictionary

This mlr3::Learner can be instantiated via the dictionary mlr3::mlr_learners or with the associated sugar function mlr3::lrn():

mlr_learners$get("classif.qda")
lrn("classif.qda")

Meta Information

• Task type: “classif”

• Predict Types: “response”, “prob”

• Feature Types: “logical”, “integer”, “numeric”, “factor”, “ordered”

• Required Packages: mlr3, mlr3learners, MASS

Parameters

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifQDA

Methods

Public methods

• LearnerClassifQDA$new()

• LearnerClassifQDA$clone()

Method new()

Creates a new instance of this R6 class.

Usage

LearnerClassifQDA$new()

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifQDA$clone(deep = FALSE)

Arguments

References

Venables WN, Ripley BD (2002). Modern Applied Statistics with S, Fourth edition. Springer, New York. ISBN 0-387-95457-0, http://www.stats.ox.ac.uk/pub/MASS4/.

See Also

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/chapters/chapter2/data_and_basic_modeling.html#sec-learners

• Package mlr3extralearners for more learners.

• Dictionary of Learners: mlr3::mlr_learners

• as.data.table(mlr_learners) for a table of available Learners in the running session (depending on the loaded packages).

• mlr3pipelines to combine learners with pre- and postprocessing steps.

• Extension packages for additional task types:

  • mlr3proba for probabilistic supervised regression and survival analysis.

  • mlr3cluster for unsupervised clustering.

• mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.

Other Learner: mlr_learners_classif.cv_glmnet, mlr_learners_classif.glmnet, mlr_learners_classif.kknn, mlr_learners_classif.lda, mlr_learners_classif.log_reg, mlr_learners_classif.multinom, mlr_learners_classif.naive_bayes, mlr_learners_classif.nnet, mlr_learners_classif.ranger, mlr_learners_classif.svm, mlr_learners_classif.xgboost, mlr_learners_regr.cv_glmnet, mlr_learners_regr.glmnet, mlr_learners_regr.kknn, mlr_learners_regr.km, mlr_learners_regr.lm, mlr_learners_regr.nnet, mlr_learners_regr.ranger, mlr_learners_regr.svm, mlr_learners_regr.xgboost

Examples

if (requireNamespace("MASS", quietly = TRUE)) {
# Define the Learner and set parameter values
learner = lrn("classif.qda")
print(learner)

# Define a Task
task = tsk("sonar")

# Create train and test set
ids = partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

# print the model
print(learner$model)

# importance method
if("importance" %in% learner$properties) print(learner$importance)

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()
}

Ranger Classification Learner

Description

Random classification forest. Calls ranger::ranger() from package ranger.

Custom mlr3 parameters

• mtry:

  • This hyperparameter can alternatively be set via our hyperparameter mtry.ratio as mtry = max(ceiling(mtry.ratio * n_features), 1). Note that mtry and mtry.ratio are mutually exclusive.

Initial parameter values

• num.threads:

  • Actual default: 2, using two threads, while also respecting environment variable R_RANGER_NUM_THREADS, options(ranger.num.threads = N), or options(Ncpus = N), with precedence in that order.

  • Adjusted value: 1.

  • Reason for change: Conflicting with parallelization via future.

Dictionary

This mlr3::Learner can be instantiated via the dictionary mlr3::mlr_learners or with the associated sugar function mlr3::lrn():

mlr_learners$get("classif.ranger")
lrn("classif.ranger")

Meta Information

• Task type: “classif”

• Predict Types: “response”, “prob”

• Feature Types: “logical”, “integer”, “numeric”, “character”, “factor”, “ordered”

• Required Packages: mlr3, mlr3learners, ranger

Parameters

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifRanger

Methods

Public methods

• LearnerClassifRanger$new()

• LearnerClassifRanger$importance()

• LearnerClassifRanger$oob_error()

• LearnerClassifRanger$selected_features()

• LearnerClassifRanger$clone()

Method new()

Creates a new instance of this R6 class.

Usage

LearnerClassifRanger$new()

Method importance()

The importance scores are extracted from the model slot variable.importance. Parameter importance.mode must be set to "impurity", "impurity_corrected", or "permutation"

Usage

LearnerClassifRanger$importance()

Returns

Named numeric().

Method oob_error()

The out-of-bag error, extracted from model slot prediction.error.

Usage

LearnerClassifRanger$oob_error()

Returns

numeric(1).

Method selected_features()

The set of features used for node splitting in the forest.

Usage

LearnerClassifRanger$selected_features()

Returns

character().

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifRanger$clone(deep = FALSE)

Arguments

References

Wright, N. M, Ziegler, Andreas (2017). “ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R.” Journal of Statistical Software, 77(1), 1–17. doi:10.18637/jss.v077.i01.

Breiman, Leo (2001). “Random Forests.” Machine Learning, 45(1), 5–32. ISSN 1573-0565, doi:10.1023/A:1010933404324.

See Also

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/chapters/chapter2/data_and_basic_modeling.html#sec-learners

• Package mlr3extralearners for more learners.

• Dictionary of Learners: mlr3::mlr_learners

• as.data.table(mlr_learners) for a table of available Learners in the running session (depending on the loaded packages).

• mlr3pipelines to combine learners with pre- and postprocessing steps.

• Extension packages for additional task types:

  • mlr3proba for probabilistic supervised regression and survival analysis.

  • mlr3cluster for unsupervised clustering.

• mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.

Other Learner: mlr_learners_classif.cv_glmnet, mlr_learners_classif.glmnet, mlr_learners_classif.kknn, mlr_learners_classif.lda, mlr_learners_classif.log_reg, mlr_learners_classif.multinom, mlr_learners_classif.naive_bayes, mlr_learners_classif.nnet, mlr_learners_classif.qda, mlr_learners_classif.svm, mlr_learners_classif.xgboost, mlr_learners_regr.cv_glmnet, mlr_learners_regr.glmnet, mlr_learners_regr.kknn, mlr_learners_regr.km, mlr_learners_regr.lm, mlr_learners_regr.nnet, mlr_learners_regr.ranger, mlr_learners_regr.svm, mlr_learners_regr.xgboost

Examples

if (requireNamespace("ranger", quietly = TRUE)) {
# Define the Learner and set parameter values
learner = lrn("classif.ranger")
print(learner)

# Define a Task
task = tsk("sonar")

# Create train and test set
ids = partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

# print the model
print(learner$model)

# importance method
if("importance" %in% learner$properties) print(learner$importance)

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()
}

Support Vector Machine

Description

Support vector machine for classification. Calls e1071::svm() from package e1071.

Dictionary

This mlr3::Learner can be instantiated via the dictionary mlr3::mlr_learners or with the associated sugar function mlr3::lrn():

mlr_learners$get("classif.svm")
lrn("classif.svm")

Meta Information

• Task type: “classif”

• Predict Types: “response”, “prob”

• Feature Types: “logical”, “integer”, “numeric”

• Required Packages: mlr3, mlr3learners, e1071

Parameters

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifSVM

Methods

Public methods

• LearnerClassifSVM$new()

• LearnerClassifSVM$clone()

Method new()

Creates a new instance of this R6 class.

Usage

LearnerClassifSVM$new()

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifSVM$clone(deep = FALSE)

Arguments

References

Cortes, Corinna, Vapnik, Vladimir (1995). “Support-vector networks.” Machine Learning, 20(3), 273–297. doi:10.1007/BF00994018.

See Also

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/chapters/chapter2/data_and_basic_modeling.html#sec-learners

• Package mlr3extralearners for more learners.

• Dictionary of Learners: mlr3::mlr_learners

• as.data.table(mlr_learners) for a table of available Learners in the running session (depending on the loaded packages).

• mlr3pipelines to combine learners with pre- and postprocessing steps.

• Extension packages for additional task types:

  • mlr3proba for probabilistic supervised regression and survival analysis.

  • mlr3cluster for unsupervised clustering.

• mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.

Other Learner: mlr_learners_classif.cv_glmnet, mlr_learners_classif.glmnet, mlr_learners_classif.kknn, mlr_learners_classif.lda, mlr_learners_classif.log_reg, mlr_learners_classif.multinom, mlr_learners_classif.naive_bayes, mlr_learners_classif.nnet, mlr_learners_classif.qda, mlr_learners_classif.ranger, mlr_learners_classif.xgboost, mlr_learners_regr.cv_glmnet, mlr_learners_regr.glmnet, mlr_learners_regr.kknn, mlr_learners_regr.km, mlr_learners_regr.lm, mlr_learners_regr.nnet, mlr_learners_regr.ranger, mlr_learners_regr.svm, mlr_learners_regr.xgboost

Examples

if (requireNamespace("e1071", quietly = TRUE)) {
# Define the Learner and set parameter values
learner = lrn("classif.svm")
print(learner)

# Define a Task
task = tsk("sonar")

# Create train and test set
ids = partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

# print the model
print(learner$model)

# importance method
if("importance" %in% learner$properties) print(learner$importance)

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()
}

Extreme Gradient Boosting Classification Learner

Description

eXtreme Gradient Boosting classification. Calls xgboost::xgb.train() from package xgboost.

If not specified otherwise, the evaluation metric is set to the default "logloss" for binary classification problems and set to "mlogloss" for multiclass problems. This was necessary to silence a deprecation warning.

Note that using the watchlist parameter directly will lead to problems when wrapping this mlr3::Learner in a mlr3pipelines GraphLearner as the preprocessing steps will not be applied to the data in the watchlist. See the section Early Stopping and Validation on how to do this.

Initial parameter values

• nrounds:

  • Actual default: no default.

  • Adjusted default: 1000.

  • Reason for change: Without a default construction of the learner would error. The lightgbm learner has a default of 1000, so we use the same here.

• nthread:

  • Actual value: Undefined, triggering auto-detection of the number of CPUs.

  • Adjusted value: 1.

  • Reason for change: Conflicting with parallelization via future.

• verbose:

  • Actual default: 1.

  • Adjusted default: 0.

  • Reason for change: Reduce verbosity.

Early Stopping and Validation

In order to monitor the validation performance during the training, you can set the ⁠$validate⁠ field of the Learner. For information on how to configure the validation set, see the Validation section of mlr3::Learner. This validation data can also be used for early stopping, which can be enabled by setting the early_stopping_rounds parameter. The final (or in the case of early stopping best) validation scores can be accessed via ⁠$internal_valid_scores⁠, and the optimal nrounds via ⁠$internal_tuned_values⁠. The internal validation measure can be set via the eval_metric parameter that can be a mlr3::Measure, a function, or a character string for the internal xgboost measures. Using an mlr3::Measure is slower than the internal xgboost measures, but allows to use the same measure for tuning and validation.

Dictionary

This mlr3::Learner can be instantiated via the dictionary mlr3::mlr_learners or with the associated sugar function mlr3::lrn():

mlr_learners$get("classif.xgboost")
lrn("classif.xgboost")

Meta Information

• Task type: “classif”

• Predict Types: “response”, “prob”

• Feature Types: “logical”, “integer”, “numeric”

• Required Packages: mlr3, mlr3learners, xgboost

Parameters

Offset

If a Task has a column with the role offset, it will automatically be used during training. The offset is incorporated through the xgboost::xgb.DMatrix interface, using the base_margin field. No offset is applied during prediction for this learner.

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifXgboost

Active bindings

Methods

Public methods

• LearnerClassifXgboost$new()

• LearnerClassifXgboost$importance()

• LearnerClassifXgboost$clone()

Method new()

Creates a new instance of this R6 class.

Usage

LearnerClassifXgboost$new()

Method importance()

The importance scores are calculated with xgboost::xgb.importance().

Usage

LearnerClassifXgboost$importance()

Returns

Named numeric().

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifXgboost$clone(deep = FALSE)

Arguments

Note

To compute on GPUs, you first need to compile xgboost yourself and link against CUDA. See https://xgboost.readthedocs.io/en/stable/build.html#building-with-gpu-support.

References

Chen, Tianqi, Guestrin, Carlos (2016). “Xgboost: A scalable tree boosting system.” In Proceedings of the 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 785–794. ACM. doi:10.1145/2939672.2939785.

See Also

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/chapters/chapter2/data_and_basic_modeling.html#sec-learners

• Package mlr3extralearners for more learners.

• Dictionary of Learners: mlr3::mlr_learners

• as.data.table(mlr_learners) for a table of available Learners in the running session (depending on the loaded packages).

• mlr3pipelines to combine learners with pre- and postprocessing steps.

• Extension packages for additional task types:

  • mlr3proba for probabilistic supervised regression and survival analysis.

  • mlr3cluster for unsupervised clustering.

• mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.

Other Learner: mlr_learners_classif.cv_glmnet, mlr_learners_classif.glmnet, mlr_learners_classif.kknn, mlr_learners_classif.lda, mlr_learners_classif.log_reg, mlr_learners_classif.multinom, mlr_learners_classif.naive_bayes, mlr_learners_classif.nnet, mlr_learners_classif.qda, mlr_learners_classif.ranger, mlr_learners_classif.svm, mlr_learners_regr.cv_glmnet, mlr_learners_regr.glmnet, mlr_learners_regr.kknn, mlr_learners_regr.km, mlr_learners_regr.lm, mlr_learners_regr.nnet, mlr_learners_regr.ranger, mlr_learners_regr.svm, mlr_learners_regr.xgboost

Examples

## Not run: 
if (requireNamespace("xgboost", quietly = TRUE)) {
# Define the Learner and set parameter values
learner = lrn("classif.xgboost")
print(learner)

# Define a Task
task = tsk("sonar")

# Create train and test set
ids = partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

# print the model
print(learner$model)

# importance method
if("importance" %in% learner$properties) print(learner$importance)

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()
}

## End(Not run)

## Not run: 
# Train learner with early stopping on spam data set
task = tsk("spam")

# use 30 percent for validation
# Set early stopping parameter
learner = lrn("classif.xgboost",
  nrounds = 100,
  early_stopping_rounds = 10,
  validate = 0.3
)

# Train learner with early stopping
learner$train(task)

# Inspect optimal nrounds and validation performance
learner$internal_tuned_values
learner$internal_valid_scores

## End(Not run)

GLM with Elastic Net Regularization Regression Learner

Description

Generalized linear models with elastic net regularization. Calls glmnet::cv.glmnet() from package glmnet.

The default for hyperparameter family is set to "gaussian".

Dictionary

This mlr3::Learner can be instantiated via the dictionary mlr3::mlr_learners or with the associated sugar function mlr3::lrn():

mlr_learners$get("regr.cv_glmnet")
lrn("regr.cv_glmnet")

Meta Information

• Task type: “regr”

• Predict Types: “response”

• Feature Types: “logical”, “integer”, “numeric”

• Required Packages: mlr3, mlr3learners, glmnet

Parameters

Offset

If a Task contains a column with the offset role, it is automatically incorporated during training via the offset argument in glmnet::glmnet(). During prediction, the offset column from the test set is used only if use_pred_offset = TRUE (default), passed via the newoffset argument in glmnet::predict.glmnet(). Otherwise, if the user sets use_pred_offset = FALSE, a zero offset is applied, effectively disabling the offset adjustment during prediction.

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrCVGlmnet

Methods

Public methods

• LearnerRegrCVGlmnet$new()

• LearnerRegrCVGlmnet$selected_features()

• LearnerRegrCVGlmnet$clone()

Method new()

Creates a new instance of this R6 class.

Usage

LearnerRegrCVGlmnet$new()

Method selected_features()

Returns the set of selected features as reported by glmnet::predict.glmnet() with type set to "nonzero".

Usage

LearnerRegrCVGlmnet$selected_features(lambda = NULL)

Arguments

Returns

(character()) of feature names.

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerRegrCVGlmnet$clone(deep = FALSE)

Arguments

References

Friedman J, Hastie T, Tibshirani R (2010). “Regularization Paths for Generalized Linear Models via Coordinate Descent.” Journal of Statistical Software, 33(1), 1–22. doi:10.18637/jss.v033.i01.

See Also

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/chapters/chapter2/data_and_basic_modeling.html#sec-learners

• Package mlr3extralearners for more learners.

• Dictionary of Learners: mlr3::mlr_learners

• as.data.table(mlr_learners) for a table of available Learners in the running session (depending on the loaded packages).

• mlr3pipelines to combine learners with pre- and postprocessing steps.

• Extension packages for additional task types:

  • mlr3proba for probabilistic supervised regression and survival analysis.

  • mlr3cluster for unsupervised clustering.

• mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.

Other Learner: mlr_learners_classif.cv_glmnet, mlr_learners_classif.glmnet, mlr_learners_classif.kknn, mlr_learners_classif.lda, mlr_learners_classif.log_reg, mlr_learners_classif.multinom, mlr_learners_classif.naive_bayes, mlr_learners_classif.nnet, mlr_learners_classif.qda, mlr_learners_classif.ranger, mlr_learners_classif.svm, mlr_learners_classif.xgboost, mlr_learners_regr.glmnet, mlr_learners_regr.kknn, mlr_learners_regr.km, mlr_learners_regr.lm, mlr_learners_regr.nnet, mlr_learners_regr.ranger, mlr_learners_regr.svm, mlr_learners_regr.xgboost

Examples

if (requireNamespace("glmnet", quietly = TRUE)) {
# Define the Learner and set parameter values
learner = lrn("regr.cv_glmnet")
print(learner)

# Define a Task
task = tsk("mtcars")

# Create train and test set
ids = partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

# print the model
print(learner$model)

# importance method
if("importance" %in% learner$properties) print(learner$importance)

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()
}

GLM with Elastic Net Regularization Regression Learner

Description

Generalized linear models with elastic net regularization. Calls glmnet::glmnet() from package glmnet.

The default for hyperparameter family is set to "gaussian".

Details

Caution: This learner is different to learners calling glmnet::cv.glmnet() in that it does not use the internal optimization of parameter lambda. Instead, lambda needs to be tuned by the user (e.g., via mlr3tuning). When lambda is tuned, the glmnet will be trained for each tuning iteration. While fitting the whole path of lambdas would be more efficient, as is done by default in glmnet::glmnet(), tuning/selecting the parameter at prediction time (using parameter s) is currently not supported in mlr3 (at least not in efficient manner). Tuning the s parameter is, therefore, currently discouraged.

When the data are i.i.d. and efficiency is key, we recommend using the respective auto-tuning counterparts in mlr_learners_classif.cv_glmnet() or mlr_learners_regr.cv_glmnet(). However, in some situations this is not applicable, usually when data are imbalanced or not i.i.d. (longitudinal, time-series) and tuning requires custom resampling strategies (blocked design, stratification).

Dictionary

This mlr3::Learner can be instantiated via the dictionary mlr3::mlr_learners or with the associated sugar function mlr3::lrn():

mlr_learners$get("regr.glmnet")
lrn("regr.glmnet")

Meta Information

• Task type: “regr”

• Predict Types: “response”

• Feature Types: “logical”, “integer”, “numeric”

• Required Packages: mlr3, mlr3learners, glmnet

Parameters

Offset

If a Task contains a column with the offset role, it is automatically incorporated during training via the offset argument in glmnet::glmnet(). During prediction, the offset column from the test set is used only if use_pred_offset = TRUE (default), passed via the newoffset argument in glmnet::predict.glmnet(). Otherwise, if the user sets use_pred_offset = FALSE, a zero offset is applied, effectively disabling the offset adjustment during prediction.

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrGlmnet

Methods

Public methods

• LearnerRegrGlmnet$new()

• LearnerRegrGlmnet$selected_features()

• LearnerRegrGlmnet$clone()

Method new()

Creates a new instance of this R6 class.

Usage

LearnerRegrGlmnet$new()

Method selected_features()

Returns the set of selected features as reported by glmnet::predict.glmnet() with type set to "nonzero".

Usage

LearnerRegrGlmnet$selected_features(lambda = NULL)

Arguments

Returns

(character()) of feature names.

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerRegrGlmnet$clone(deep = FALSE)

Arguments

References

Friedman J, Hastie T, Tibshirani R (2010). “Regularization Paths for Generalized Linear Models via Coordinate Descent.” Journal of Statistical Software, 33(1), 1–22. doi:10.18637/jss.v033.i01.

See Also

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/chapters/chapter2/data_and_basic_modeling.html#sec-learners

• Package mlr3extralearners for more learners.

• Dictionary of Learners: mlr3::mlr_learners

• as.data.table(mlr_learners) for a table of available Learners in the running session (depending on the loaded packages).

• mlr3pipelines to combine learners with pre- and postprocessing steps.

• Extension packages for additional task types:

  • mlr3proba for probabilistic supervised regression and survival analysis.

  • mlr3cluster for unsupervised clustering.

• mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.

Other Learner: mlr_learners_classif.cv_glmnet, mlr_learners_classif.glmnet, mlr_learners_classif.kknn, mlr_learners_classif.lda, mlr_learners_classif.log_reg, mlr_learners_classif.multinom, mlr_learners_classif.naive_bayes, mlr_learners_classif.nnet, mlr_learners_classif.qda, mlr_learners_classif.ranger, mlr_learners_classif.svm, mlr_learners_classif.xgboost, mlr_learners_regr.cv_glmnet, mlr_learners_regr.kknn, mlr_learners_regr.km, mlr_learners_regr.lm, mlr_learners_regr.nnet, mlr_learners_regr.ranger, mlr_learners_regr.svm, mlr_learners_regr.xgboost

Examples

if (requireNamespace("glmnet", quietly = TRUE)) {
# Define the Learner and set parameter values
learner = lrn("regr.glmnet")
print(learner)

# Define a Task
task = tsk("mtcars")

# Create train and test set
ids = partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

# print the model
print(learner$model)

# importance method
if("importance" %in% learner$properties) print(learner$importance)

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()
}

k-Nearest-Neighbor Regression Learner

Description

k-Nearest-Neighbor regression. Calls kknn::kknn() from package kknn.

Initial parameter values

• store_model:

  • See note.

Dictionary

This mlr3::Learner can be instantiated via the dictionary mlr3::mlr_learners or with the associated sugar function mlr3::lrn():

mlr_learners$get("regr.kknn")
lrn("regr.kknn")

Meta Information

• Task type: “regr”

• Predict Types: “response”

• Feature Types: “logical”, “integer”, “numeric”, “factor”, “ordered”

• Required Packages: mlr3, mlr3learners, kknn

Parameters

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrKKNN

Methods

Public methods

• LearnerRegrKKNN$new()

• LearnerRegrKKNN$clone()

Method new()

Creates a new instance of this R6 class.

Usage

LearnerRegrKKNN$new()

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerRegrKKNN$clone(deep = FALSE)

Arguments

Note

There is no training step for k-NN models, just storing the training data to process it during the predict step. Therefore, ⁠$model⁠ returns a list with the following elements:

• formula: Formula for calling kknn::kknn() during ⁠$predict()⁠.

• data: Training data for calling kknn::kknn() during ⁠$predict()⁠.

• pv: Training parameters for calling kknn::kknn() during ⁠$predict()⁠.

• kknn: Model as returned by kknn::kknn(), only available after ⁠$predict()⁠ has been called. This is not stored by default, you must set hyperparameter store_model to TRUE.

References

Hechenbichler, Klaus, Schliep, Klaus (2004). “Weighted k-nearest-neighbor techniques and ordinal classification.” Technical Report Discussion Paper 399, SFB 386, Ludwig-Maximilians University Munich. doi:10.5282/ubm/epub.1769.

Samworth, J R (2012). “Optimal weighted nearest neighbour classifiers.” The Annals of Statistics, 40(5), 2733–2763. doi:10.1214/12-AOS1049.

Cover, Thomas, Hart, Peter (1967). “Nearest neighbor pattern classification.” IEEE transactions on information theory, 13(1), 21–27. doi:10.1109/TIT.1967.1053964.

See Also

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/chapters/chapter2/data_and_basic_modeling.html#sec-learners

• Package mlr3extralearners for more learners.

• Dictionary of Learners: mlr3::mlr_learners

• as.data.table(mlr_learners) for a table of available Learners in the running session (depending on the loaded packages).

• mlr3pipelines to combine learners with pre- and postprocessing steps.

• Extension packages for additional task types:

  • mlr3proba for probabilistic supervised regression and survival analysis.

  • mlr3cluster for unsupervised clustering.

• mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.

Other Learner: mlr_learners_classif.cv_glmnet, mlr_learners_classif.glmnet, mlr_learners_classif.kknn, mlr_learners_classif.lda, mlr_learners_classif.log_reg, mlr_learners_classif.multinom, mlr_learners_classif.naive_bayes, mlr_learners_classif.nnet, mlr_learners_classif.qda, mlr_learners_classif.ranger, mlr_learners_classif.svm, mlr_learners_classif.xgboost, mlr_learners_regr.cv_glmnet, mlr_learners_regr.glmnet, mlr_learners_regr.km, mlr_learners_regr.lm, mlr_learners_regr.nnet, mlr_learners_regr.ranger, mlr_learners_regr.svm, mlr_learners_regr.xgboost

Examples

if (requireNamespace("kknn", quietly = TRUE)) {
# Define the Learner and set parameter values
learner = lrn("regr.kknn")
print(learner)

# Define a Task
task = tsk("mtcars")

# Create train and test set
ids = partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

# print the model
print(learner$model)

# importance method
if("importance" %in% learner$properties) print(learner$importance)

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()
}

Kriging Regression Learner

Description

Kriging regression. Calls DiceKriging::km() from package DiceKriging.

• The predict type hyperparameter "type" defaults to "SK" (simple kriging).

• The additional hyperparameter nugget.stability is used to overwrite the hyperparameter nugget with nugget.stability * var(y) before training to improve the numerical stability. We recommend a value of 1e-8.

• The additional hyperparameter jitter can be set to add ⁠N(0, [jitter])⁠-distributed noise to the data before prediction to avoid perfect interpolation. We recommend a value of 1e-12.

Dictionary

This mlr3::Learner can be instantiated via the dictionary mlr3::mlr_learners or with the associated sugar function mlr3::lrn():

mlr_learners$get("regr.km")
lrn("regr.km")

Meta Information

• Task type: “regr”

• Predict Types: “response”, “se”

• Feature Types: “logical”, “integer”, “numeric”

• Required Packages: mlr3, mlr3learners, DiceKriging

Parameters

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrKM

Methods

Public methods

• LearnerRegrKM$new()

• LearnerRegrKM$clone()

Method new()

Creates a new instance of this R6 class.

Usage

LearnerRegrKM$new()

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerRegrKM$clone(deep = FALSE)

Arguments

References

Roustant O, Ginsbourger D, Deville Y (2012). “DiceKriging, DiceOptim: Two R Packages for the Analysis of Computer Experiments by Kriging-Based Metamodeling and Optimization.” Journal of Statistical Software, 51(1), 1–55. doi:10.18637/jss.v051.i01.

See Also

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/chapters/chapter2/data_and_basic_modeling.html#sec-learners

• Package mlr3extralearners for more learners.

• Dictionary of Learners: mlr3::mlr_learners

• as.data.table(mlr_learners) for a table of available Learners in the running session (depending on the loaded packages).

• mlr3pipelines to combine learners with pre- and postprocessing steps.

• Extension packages for additional task types:

  • mlr3proba for probabilistic supervised regression and survival analysis.

  • mlr3cluster for unsupervised clustering.

• mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.

Other Learner: mlr_learners_classif.cv_glmnet, mlr_learners_classif.glmnet, mlr_learners_classif.kknn, mlr_learners_classif.lda, mlr_learners_classif.log_reg, mlr_learners_classif.multinom, mlr_learners_classif.naive_bayes, mlr_learners_classif.nnet, mlr_learners_classif.qda, mlr_learners_classif.ranger, mlr_learners_classif.svm, mlr_learners_classif.xgboost, mlr_learners_regr.cv_glmnet, mlr_learners_regr.glmnet, mlr_learners_regr.kknn, mlr_learners_regr.lm, mlr_learners_regr.nnet, mlr_learners_regr.ranger, mlr_learners_regr.svm, mlr_learners_regr.xgboost

Examples

if (requireNamespace("DiceKriging", quietly = TRUE)) {
# Define the Learner and set parameter values
learner = lrn("regr.km")
print(learner)

# Define a Task
task = tsk("mtcars")

# Create train and test set
ids = partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

# print the model
print(learner$model)

# importance method
if("importance" %in% learner$properties) print(learner$importance)

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()
}

Linear Model Regression Learner

Description

Ordinary linear regression. Calls stats::lm().

Offset

If a Task has a column with the role offset, it will automatically be used during training. The offset is incorporated through the formula interface to ensure compatibility with stats::lm(). We add it to the model formula as ⁠offset(<column_name>)⁠ and also include it in the training data. During prediction, the default behavior is to use the offset column from the test set (enabled by use_pred_offset = TRUE). Otherwise, if the user sets use_pred_offset = FALSE, a zero offset is applied, effectively disabling the offset adjustment during prediction.

Dictionary

This mlr3::Learner can be instantiated via the dictionary mlr3::mlr_learners or with the associated sugar function mlr3::lrn():

mlr_learners$get("regr.lm")
lrn("regr.lm")

Meta Information

• Task type: “regr”

• Predict Types: “response”, “se”

• Feature Types: “logical”, “integer”, “numeric”, “character”, “factor”

• Required Packages: mlr3, mlr3learners, 'stats'

Parameters

Contrasts

To ensure reproducibility, this learner always uses the default contrasts:

• contr.treatment() for unordered factors, and

• contr.poly() for ordered factors.

Setting the option "contrasts" does not have any effect. Instead, set the respective hyperparameter or use mlr3pipelines to create dummy features.

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrLM

Methods

Public methods

• LearnerRegrLM$new()

• LearnerRegrLM$clone()

Method new()

Creates a new instance of this R6 class.

Usage

LearnerRegrLM$new()

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerRegrLM$clone(deep = FALSE)

Arguments

See Also

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/chapters/chapter2/data_and_basic_modeling.html#sec-learners

• Package mlr3extralearners for more learners.

• Dictionary of Learners: mlr3::mlr_learners

• as.data.table(mlr_learners) for a table of available Learners in the running session (depending on the loaded packages).

• mlr3pipelines to combine learners with pre- and postprocessing steps.

• Extension packages for additional task types:

  • mlr3proba for probabilistic supervised regression and survival analysis.

  • mlr3cluster for unsupervised clustering.

• mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.

Other Learner: mlr_learners_classif.cv_glmnet, mlr_learners_classif.glmnet, mlr_learners_classif.kknn, mlr_learners_classif.lda, mlr_learners_classif.log_reg, mlr_learners_classif.multinom, mlr_learners_classif.naive_bayes, mlr_learners_classif.nnet, mlr_learners_classif.qda, mlr_learners_classif.ranger, mlr_learners_classif.svm, mlr_learners_classif.xgboost, mlr_learners_regr.cv_glmnet, mlr_learners_regr.glmnet, mlr_learners_regr.kknn, mlr_learners_regr.km, mlr_learners_regr.nnet, mlr_learners_regr.ranger, mlr_learners_regr.svm, mlr_learners_regr.xgboost

Examples

if (requireNamespace("stats", quietly = TRUE)) {
# Define the Learner and set parameter values
learner = lrn("regr.lm")
print(learner)

# Define a Task
task = tsk("mtcars")

# Create train and test set
ids = partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

# print the model
print(learner$model)

# importance method
if("importance" %in% learner$properties) print(learner$importance)

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()
}

Neural Network Regression Learner

Description

Single Layer Neural Network. Calls nnet::nnet.formula() from package nnet.

Note that modern neural networks with multiple layers are connected via package mlr3torch.

Dictionary

This mlr3::Learner can be instantiated via the dictionary mlr3::mlr_learners or with the associated sugar function mlr3::lrn():

mlr_learners$get("regr.nnet")
lrn("regr.nnet")

Meta Information

• Task type: “regr”

• Predict Types: “response”

• Feature Types: “logical”, “integer”, “numeric”, “factor”, “ordered”

• Required Packages: mlr3, mlr3learners, nnet

Parameters

Initial parameter values

• size:

  • Adjusted default: 3L.

  • Reason for change: no default in nnet().

Custom mlr3 parameters

• formula: if not provided, the formula is set to task$formula().

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrNnet

Methods

Public methods

• LearnerRegrNnet$new()

• LearnerRegrNnet$clone()

Method new()

Creates a new instance of this R6 class.

Usage

LearnerRegrNnet$new()

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerRegrNnet$clone(deep = FALSE)

Arguments

References

Ripley BD (1996). Pattern Recognition and Neural Networks. Cambridge University Press. doi:10.1017/cbo9780511812651.

See Also

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/chapters/chapter2/data_and_basic_modeling.html#sec-learners

• Package mlr3extralearners for more learners.

• Dictionary of Learners: mlr3::mlr_learners

• as.data.table(mlr_learners) for a table of available Learners in the running session (depending on the loaded packages).

• mlr3pipelines to combine learners with pre- and postprocessing steps.

• Extension packages for additional task types:

  • mlr3proba for probabilistic supervised regression and survival analysis.

  • mlr3cluster for unsupervised clustering.

• mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.

Other Learner: mlr_learners_classif.cv_glmnet, mlr_learners_classif.glmnet, mlr_learners_classif.kknn, mlr_learners_classif.lda, mlr_learners_classif.log_reg, mlr_learners_classif.multinom, mlr_learners_classif.naive_bayes, mlr_learners_classif.nnet, mlr_learners_classif.qda, mlr_learners_classif.ranger, mlr_learners_classif.svm, mlr_learners_classif.xgboost, mlr_learners_regr.cv_glmnet, mlr_learners_regr.glmnet, mlr_learners_regr.kknn, mlr_learners_regr.km, mlr_learners_regr.lm, mlr_learners_regr.ranger, mlr_learners_regr.svm, mlr_learners_regr.xgboost

Examples

if (requireNamespace("nnet", quietly = TRUE)) {
# Define the Learner and set parameter values
learner = lrn("regr.nnet")
print(learner)

# Define a Task
task = tsk("mtcars")

# Create train and test set
ids = partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

# print the model
print(learner$model)

# importance method
if("importance" %in% learner$properties) print(learner$importance)

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()
}

Ranger Regression Learner

Description

Random regression forest. Calls ranger::ranger() from package ranger.

Dictionary

This mlr3::Learner can be instantiated via the dictionary mlr3::mlr_learners or with the associated sugar function mlr3::lrn():

mlr_learners$get("regr.ranger")
lrn("regr.ranger")

Meta Information

• Task type: “regr”

• Predict Types: “response”, “se”, “quantiles”

• Feature Types: “logical”, “integer”, “numeric”, “character”, “factor”, “ordered”

• Required Packages: mlr3, mlr3learners, ranger

Parameters

Custom mlr3 parameters

• mtry:

  • This hyperparameter can alternatively be set via our hyperparameter mtry.ratio as mtry = max(ceiling(mtry.ratio * n_features), 1). Note that mtry and mtry.ratio are mutually exclusive.

Initial parameter values

• num.threads:

  • Actual default: 2, using two threads, while also respecting environment variable R_RANGER_NUM_THREADS, options(ranger.num.threads = N), or options(Ncpus = N), with precedence in that order.

  • Adjusted value: 1.

  • Reason for change: Conflicting with parallelization via future.

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrRanger

Methods

Public methods

• LearnerRegrRanger$new()

• LearnerRegrRanger$importance()

• LearnerRegrRanger$oob_error()

• LearnerRegrRanger$selected_features()

• LearnerRegrRanger$clone()

Method new()

Creates a new instance of this R6 class.

Usage

LearnerRegrRanger$new()

Method importance()

The importance scores are extracted from the model slot variable.importance. Parameter importance.mode must be set to "impurity", "impurity_corrected", or "permutation"

Usage

LearnerRegrRanger$importance()

Returns

Named numeric().

Method oob_error()

The out-of-bag error, extracted from model slot prediction.error.

Usage

LearnerRegrRanger$oob_error()

Returns

numeric(1).

Method selected_features()

The set of features used for node splitting in the forest.

Usage

LearnerRegrRanger$selected_features()

Returns

character().

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerRegrRanger$clone(deep = FALSE)

Arguments

References

Wright, N. M, Ziegler, Andreas (2017). “ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R.” Journal of Statistical Software, 77(1), 1–17. doi:10.18637/jss.v077.i01.

Breiman, Leo (2001). “Random Forests.” Machine Learning, 45(1), 5–32. ISSN 1573-0565, doi:10.1023/A:1010933404324.

See Also

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/chapters/chapter2/data_and_basic_modeling.html#sec-learners

• Package mlr3extralearners for more learners.

• Dictionary of Learners: mlr3::mlr_learners

• as.data.table(mlr_learners) for a table of available Learners in the running session (depending on the loaded packages).

• mlr3pipelines to combine learners with pre- and postprocessing steps.

• Extension packages for additional task types:

  • mlr3proba for probabilistic supervised regression and survival analysis.

  • mlr3cluster for unsupervised clustering.

• mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.

Other Learner: mlr_learners_classif.cv_glmnet, mlr_learners_classif.glmnet, mlr_learners_classif.kknn, mlr_learners_classif.lda, mlr_learners_classif.log_reg, mlr_learners_classif.multinom, mlr_learners_classif.naive_bayes, mlr_learners_classif.nnet, mlr_learners_classif.qda, mlr_learners_classif.ranger, mlr_learners_classif.svm, mlr_learners_classif.xgboost, mlr_learners_regr.cv_glmnet, mlr_learners_regr.glmnet, mlr_learners_regr.kknn, mlr_learners_regr.km, mlr_learners_regr.lm, mlr_learners_regr.nnet, mlr_learners_regr.svm, mlr_learners_regr.xgboost

Examples

if (requireNamespace("ranger", quietly = TRUE)) {
# Define the Learner and set parameter values
learner = lrn("regr.ranger")
print(learner)

# Define a Task
task = tsk("mtcars")

# Create train and test set
ids = partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

# print the model
print(learner$model)

# importance method
if("importance" %in% learner$properties) print(learner$importance)

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()
}

Support Vector Machine

Description

Support vector machine for regression. Calls e1071::svm() from package e1071.

Dictionary

This mlr3::Learner can be instantiated via the dictionary mlr3::mlr_learners or with the associated sugar function mlr3::lrn():

mlr_learners$get("regr.svm")
lrn("regr.svm")

Meta Information

• Task type: “regr”

• Predict Types: “response”

• Feature Types: “logical”, “integer”, “numeric”

• Required Packages: mlr3, mlr3learners, e1071

Parameters

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrSVM

Methods

Public methods

• LearnerRegrSVM$new()

• LearnerRegrSVM$clone()

Method new()

Creates a new instance of this R6 class.

Usage