GLM with Elastic Net Regularization Regression Learner

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

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

Caution: This learner is different to cv_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("regr.glmnet")
lrn("regr.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::LearnerRegr -> LearnerRegrGlmnet

Methods

Public methods

• LearnerRegrGlmnet$new()

• LearnerRegrGlmnet$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

LearnerRegrGlmnet$new()

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Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerRegrGlmnet$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

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

  # available parameters:
learner$param_set$ids()
}
#> <LearnerRegrGlmnet:regr.glmnet>
#> * Model: -
#> * Parameters: family=gaussian
#> * Packages: glmnet
#> * Predict Type: response
#> * Feature types: logical, integer, numeric
#> * Properties: weights
#>  [1] "family"           "offset"           "alpha"            "type.measure"    
#>  [5] "s"                "nlambda"          "lambda.min.ratio" "lambda"          
#>  [9] "standardize"      "intercept"        "thresh"           "dfmax"           
#> [13] "pmax"             "exclude"          "penalty.factor"   "lower.limits"    
#> [17] "upper.limits"     "maxit"            "type.gaussian"    "type.logistic"   
#> [21] "type.multinomial" "keep"             "parallel"         "trace.it"        
#> [25] "alignment"        "grouped"          "relax"            "fdev"            
#> [29] "devmax"           "eps"              "epsnr"            "big"             
#> [33] "mnlam"            "pmin"             "exmx"             "prec"            
#> [37] "mxit"             "mxitnr"           "newoffset"        "predict.gamma"   
#> [41] "exact"            "gamma"