Create multiple learners at once.

Small helper function that can save some typing when creating mutiple learner objects. Calls makeLearner multiple times internally.

makeLearners(cls, ids = NULL, type = NULL, ...)

Arguments

cls	(character) Classes of learners.
ids	(character) Id strings. Must be unique. Default is cls.
type	(character(1)) Shortcut to prepend type string to cls so one can set cls = "rpart". Default is NULL, i.e., this is not used.
...	(any) Optional named (hyper)parameters. If you want to set specific hyperparameters for a learner during model creation, these should go here. You can get a list of available hyperparameters using getParamSet(<learner>). Alternatively hyperparameters can be given using the par.vals argument but ... should be preferred!

Value

(named list of Learner). Named by ids.

See also

Other learner: LearnerProperties, getClassWeightParam(), getHyperPars(), getLearnerId(), getLearnerNote(), getLearnerPackages(), getLearnerParVals(), getLearnerParamSet(), getLearnerPredictType(), getLearnerShortName(), getLearnerType(), getParamSet(), helpLearnerParam(), helpLearner(), makeLearner(), removeHyperPars(), setHyperPars(), setId(), setLearnerId(), setPredictThreshold(), setPredictType()

Examples

makeLearners(c("rpart", "lda"), type = "classif", predict.type = "prob")
#> $classif.rpart
#> Learner classif.rpart from package rpart
#> Type: classif
#> Name: Decision Tree; Short name: rpart
#> Class: classif.rpart
#> Properties: twoclass,multiclass,missings,numerics,factors,ordered,prob,weights,featimp
#> Predict-Type: prob
#> Hyperparameters: xval=0
#> 
#> 
#> $classif.lda
#> Learner classif.lda from package MASS
#> Type: classif
#> Name: Linear Discriminant Analysis; Short name: lda
#> Class: classif.lda
#> Properties: twoclass,multiclass,numerics,factors,prob
#> Predict-Type: prob
#> Hyperparameters: 
#> 
#>