mlr3learners (version 0.5.2)

mlr_learners_classif.cv_glmnet: GLM with Elastic Net Regularization Classification Learner

Description

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	Range	Levels
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	list	NULL	-	-
gamma	list	-	-	-
grouped	logical	TRUE	-	TRUE, FALSE
intercept	logical	TRUE	-	TRUE, FALSE
keep	logical	FALSE	-	TRUE, FALSE
lambda.min.ratio	numeric	-	\([0, 1]\)	-
lambda	list	-	-	-
lower.limits	list	-	-	-
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	list	NULL	-	-
parallel	logical	FALSE	-	TRUE, FALSE
penalty.factor	list	-	-	-
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	list	-	-	-

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.

Super classes

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

Methods

Public methods

• LearnerClassifCVGlmnet$new()

• LearnerClassifCVGlmnet$selected_features()

• LearnerClassifCVGlmnet$clone()

• 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.

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. 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, mlr_learners_surv.cv_glmnet, mlr_learners_surv.glmnet, mlr_learners_surv.ranger, mlr_learners_surv.xgboost

Examples

# NOT RUN {
if (requireNamespace("glmnet", quietly = TRUE)) {
  learner = mlr3::lrn("classif.cv_glmnet")
  print(learner)

  # available parameters:
learner$param_set$ids()
}
# }