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

, |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 |- | |\([0, \infty)\) |, |eps |numeric |1e-06 | |\([0, 1]\) |, |epsnr |numeric |1e-08 | |\([0, 1]\) |, |exclude |integer |- | |\([1, \infty)\) |, |exmx |numeric |250 | |\((-\infty, \infty)\) |, |family |character |gaussian |gaussian, poisson |- |, |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 |untyped |- | |- |, |lambda.min.ratio |numeric |- | |\([0, 1]\) |, |lower.limits |untyped |- | |- |, |maxit |integer |100000 | |\([1, \infty)\) |, |mnlam |integer |5 | |\([1, \infty)\) |, |mxit |integer |100 | |\([1, \infty)\) |, |mxitnr |integer |25 | |\([1, \infty)\) |, |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 |- | |- |

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

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

Super classes

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

Methods

Inherited methods


Method new()

Creates a new instance of this R6 class.

Usage


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

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

LearnerRegrCVGlmnet$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

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

  # available parameters:
learner$param_set$ids()
}
#> <LearnerRegrCVGlmnet:regr.cv_glmnet>
#> * Model: -
#> * Parameters: family=gaussian
#> * Packages: mlr3, mlr3learners, glmnet
#> * Predict Type: response
#> * Feature types: logical, integer, numeric
#> * Properties: selected_features, weights
#>  [1] "alignment"            "alpha"                "big"                 
#>  [4] "devmax"               "dfmax"                "eps"                 
#>  [7] "epsnr"                "exclude"              "exmx"                
#> [10] "family"               "fdev"                 "foldid"              
#> [13] "gamma"                "grouped"              "intercept"           
#> [16] "keep"                 "lambda"               "lambda.min.ratio"    
#> [19] "lower.limits"         "maxit"                "mnlam"               
#> [22] "mxit"                 "mxitnr"               "nfolds"              
#> [25] "nlambda"              "offset"               "parallel"            
#> [28] "penalty.factor"       "pmax"                 "pmin"                
#> [31] "prec"                 "predict.gamma"        "relax"               
#> [34] "s"                    "standardize"          "standardize.response"
#> [37] "thresh"               "trace.it"             "type.gaussian"       
#> [40] "type.logistic"        "type.measure"         "type.multinomial"    
#> [43] "upper.limits"