GLM with Elastic Net Regularization Classification Learner

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.

Dictionary

This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function 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

, |Id |Type |Default |Levels |Range |, |:--------------------|:---------|:----------|:------------------------------|:------------------------------------|, |alignment |character |lambda |lambda, fraction |- |, |alpha |numeric |1 | |\([0, 1]\) |, |big |numeric |9.9e+35 | |\((-\infty, \infty)\) |, |devmax |numeric |0.999 | |\([0, 1]\) |, |dfmax |integer |- | |\([0, \infty)\) |, |epsnr |numeric |1e-08 | |\([0, 1]\) |, |eps |numeric |1e-06 | |\([0, 1]\) |, |exclude |integer |- | |\([1, \infty)\) |, |exmx |numeric |250 | |\((-\infty, \infty)\) |, |fdev |numeric |1e-05 | |\([0, 1]\) |, |foldid |untyped | | |- |, |gamma |untyped |- | |- |, |grouped |logical |TRUE |TRUE, FALSE |- |, |intercept |logical |TRUE |TRUE, FALSE |- |, |keep |logical |FALSE |TRUE, FALSE |- |, |lambda.min.ratio |numeric |- | |\([0, 1]\) |, |lambda |untyped |- | |- |, |lower.limits |untyped |- | |- |, |maxit |integer |100000 | |\([1, \infty)\) |, |mnlam |integer |5 | |\([1, \infty)\) |, |mxitnr |integer |25 | |\([1, \infty)\) |, |mxit |integer |100 | |\([1, \infty)\) |, |nfolds |integer |10 | |\([3, \infty)\) |, |nlambda |integer |100 | |\([1, \infty)\) |, |offset |untyped | | |- |, |parallel |logical |FALSE |TRUE, FALSE |- |, |penalty.factor |untyped |- | |- |, |pmax |integer |- | |\([0, \infty)\) |, |pmin |numeric |1e-09 | |\([0, 1]\) |, |prec |numeric |1e-10 | |\((-\infty, \infty)\) |, |predict.gamma |numeric |gamma.1se | |\((-\infty, \infty)\) |, |relax |logical |FALSE |TRUE, FALSE |- |, |s |numeric |lambda.1se | |\([0, \infty)\) |, |standardize |logical |TRUE |TRUE, FALSE |- |, |standardize.response |logical |FALSE |TRUE, FALSE |- |, |thresh |numeric |1e-07 | |\([0, \infty)\) |, |trace.it |integer |0 | |\([0, 1]\) |, |type.gaussian |character |- |covariance, naive |- |, |type.logistic |character |- |Newton, modified.Newton |- |, |type.measure |character |deviance |deviance, class, auc, mse, mae |- |, |type.multinomial |character |- |ungrouped, grouped |- |, |upper.limits |untyped |- | |- |

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.

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/basics.html#learners

• Package mlr3extralearners for more learners.

• Dictionary of Learners: 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.ranger, mlr_learners_regr.svm, mlr_learners_regr.xgboost

Super classes

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

Methods

Public methods

• LearnerClassifCVGlmnet$new()

• LearnerClassifCVGlmnet$selected_features()

• LearnerClassifCVGlmnet$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$format()
• mlr3::Learner$help()
• mlr3::Learner$predict()
• mlr3::Learner$predict_newdata()
• mlr3::Learner$print()
• mlr3::Learner$reset()
• mlr3::Learner$train()

---

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

lambda

(numeric(1))
Custom lambda, defaults to the active lambda depending on parameter set.

Returns

(character()) of feature names.

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifCVGlmnet$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

if (requireNamespace("glmnet", quietly = TRUE)) {
  learner = mlr3::lrn("classif.cv_glmnet")
  print(learner)

  # available parameters:
learner$param_set$ids()
}
#> <LearnerClassifCVGlmnet:classif.cv_glmnet>
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3learners, glmnet
#> * Predict Type: response
#> * Feature types: logical, integer, numeric
#> * Properties: multiclass, selected_features, twoclass, weights
#>  [1] "alignment"            "alpha"                "big"                 
#>  [4] "devmax"               "dfmax"                "epsnr"               
#>  [7] "eps"                  "exclude"              "exmx"                
#> [10] "fdev"                 "foldid"               "gamma"               
#> [13] "grouped"              "intercept"            "keep"                
#> [16] "lambda.min.ratio"     "lambda"               "lower.limits"        
#> [19] "maxit"                "mnlam"                "mxitnr"              
#> [22] "mxit"                 "nfolds"               "nlambda"             
#> [25] "offset"               "parallel"             "penalty.factor"      
#> [28] "pmax"                 "pmin"                 "prec"                
#> [31] "predict.gamma"        "relax"                "s"                   
#> [34] "standardize"          "standardize.response" "thresh"              
#> [37] "trace.it"             "type.gaussian"        "type.logistic"       
#> [40] "type.measure"         "type.multinomial"     "upper.limits"