GLM with Elastic Net Regularization Classification Learner

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

Caution: This learner is different to _glmnet in that it does not use the internal optimization of lambda. The parameter needs to be tuned by the user. Essentially, one needs to tune parameter s which is used at predict-time.

See https://stackoverflow.com/questions/50995525/ for more information.

Dictionary

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

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

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

Dictionary of Learners: mlr3::mlr_learners

Super classes

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

Methods

Public methods

• LearnerClassifGlmnet$new()

• LearnerClassifGlmnet$clone()

Inherited methods

• mlr3::Learner$format()

• mlr3::Learner$help()

• mlr3::Learner$predict()

• mlr3::Learner$predict_newdata()

• mlr3::Learner$print()

• mlr3::Learner$reset()

• mlr3::Learner$train()

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Method new()

Creates a new instance of this R6 class.

Usage

LearnerClassifGlmnet$new()

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Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifGlmnet$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

if (requireNamespace("glmnet")) {
  learner = mlr3::lrn("classif.glmnet")
  print(learner)

  # available parameters:
learner$param_set$ids()
}
#> <LearnerClassifGlmnet:classif.glmnet>
#> * Model: -
#> * Parameters: list()
#> * Packages: glmnet
#> * Predict Type: response
#> * Feature types: logical, integer, numeric
#> * Properties: multiclass, twoclass, weights
#>  [1] "alpha"            "type.measure"     "s"                "lambda.min.ratio"
#>  [5] "lambda"           "standardize"      "intercept"        "thresh"          
#>  [9] "dfmax"            "pmax"             "exclude"          "penalty.factor"  
#> [13] "lower.limits"     "upper.limits"     "maxit"            "type.logistic"   
#> [17] "type.multinomial" "keep"             "parallel"         "trace.it"        
#> [21] "alignment"        "grouped"          "offset"           "relax"           
#> [25] "fdev"             "devmax"           "eps"              "epsnr"           
#> [29] "big"              "mnlam"            "pmin"             "exmx"            
#> [33] "prec"             "mxit"             "mxitnr"           "newoffset"       
#> [37] "exact"            "gamma"