GLM with Elastic Net Regularization Regression Learner

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

Supported family values are "gaussian" and "poisson". The default for the hyperparameter family is "gaussian".

Dictionary

This mlr3::Learner can be instantiated via the dictionary mlr3::mlr_learners or with the associated sugar function mlr3::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
lambda	untyped	NULL		-
type.measure	character	deviance	deviance, mse, mae	-
nfolds	integer	10		$[3, \infty)$
foldid	untyped	NULL		-
alignment	character	lambda	lambda, fraction	-
grouped	logical	TRUE	TRUE, FALSE	-
keep	logical	FALSE	TRUE, FALSE	-
parallel	logical	FALSE	TRUE, FALSE	-
gamma	untyped	c(0, 0.25, 0.5, 0.75, 1)		-
relax	logical	FALSE	TRUE, FALSE	-
trace.it	integer	0		$[0, 1]$
family	character	-	gaussian, poisson	-
alpha	numeric	1		$[0, 1]$
nlambda	integer	100		$[1, \infty)$
lambda.min.ratio	numeric	-		$[0, 1]$
standardize	logical	TRUE	TRUE, FALSE	-
intercept	logical	TRUE	TRUE, FALSE	-
exclude	untyped	NULL		-
penalty.factor	untyped	-		-
lower.limits	untyped	-Inf		-
upper.limits	untyped	Inf		-
type.gaussian	character	-	covariance, naive	-
maxp	integer	-		$[1, \infty)$
path	logical	FALSE	TRUE, FALSE	-
fdev	numeric	1e-05		$[0, 1]$
devmax	numeric	0.999		$[0, 1]$
eps	numeric	1e-06		$[0, 1]$
big	numeric	9.9e+35		$(-\infty, \infty)$
mnlam	integer	5		$(-\infty, \infty)$
pmin	numeric	1e-09		$[0, 1]$
exmx	numeric	250		$(-\infty, \infty)$
prec	numeric	1e-10		$(-\infty, \infty)$
mxit	integer	100		$[1, \infty)$
epsnr	numeric	1e-06		$[0, 1]$
mxitnr	integer	25		$[1, \infty)$
thresh	numeric	1e-07		$[0, \infty)$
maxit	integer	100000		$[1, \infty)$
dfmax	integer	NULL		$(-\infty, \infty)$
pmax	integer	NULL		$(-\infty, \infty)$
s	numeric	lambda.1se		$[0, \infty)$
predict.gamma	numeric	gamma.1se		$[0, 1]$
exact	logical	FALSE	TRUE, FALSE	-
use_pred_offset	logical	-	TRUE, FALSE	-
seed	integer	-		$(-\infty, \infty)$

Custom mlr3 parameters

seed:
- Optional integer used to seed the call to glmnet::cv.glmnet(), making its random fold assignment, and therefore the selected lambda, reproducible.
- The global random state is reset afterwards, so it is left unchanged.
- Defaults to NA, in which case no seed is set and the global random state is used.

Offset

If a Task contains a column with the offset role, it is automatically incorporated during training via the offset argument in glmnet::glmnet(). During prediction, the offset column from the test set is used only if use_pred_offset = TRUE (default), passed via the newoffset argument in glmnet::predict.glmnet(). Otherwise, if the user sets use_pred_offset = FALSE, a zero offset is applied, effectively disabling the offset adjustment during prediction.

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 .

Super classes

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

Methods

Public methods

LearnerRegrCVGlmnet$new()
LearnerRegrCVGlmnet$selected_features()
LearnerRegrCVGlmnet$clone()

Inherited methods

`LearnerRegrCVGlmnet$new()`

Creates a new instance of this R6 class.

Usage

LearnerRegrCVGlmnet$new()

`LearnerRegrCVGlmnet$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.

`LearnerRegrCVGlmnet$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

# Define the Learner and set parameter values
learner = lrn("regr.cv_glmnet")
print(learner)
#> 
#> ── <LearnerRegrCVGlmnet> (regr.cv_glmnet): GLM with Elastic Net Regularization ─
#> • Model: -
#> • Parameters: family=gaussian, use_pred_offset=TRUE, seed=NA
#> • Packages: mlr3, mlr3learners, and glmnet
#> • Predict Types: [response]
#> • Feature Types: logical, integer, and numeric
#> • Encapsulation: none (fallback: -)
#> • Properties: offset, selected_features, and weights
#> • Other settings: use_weights = 'use', predict_raw = 'FALSE'

# Define a Task
task = tsk("mtcars")

# Create train and test set
ids = partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)
#> Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per fold

# Print the model
print(learner$model)
#> 
#> Call:  glmnet::cv.glmnet(x = data, y = target, family = "gaussian") 
#> 
#> Measure: Mean-Squared Error 
#> 
#>     Lambda Index Measure    SE Nonzero
#> min 0.2398    35   9.745 3.084       7
#> 1se 1.8570    13  12.761 4.231       3

# Importance method
if ("importance" %in% learner$properties) print(learner$importance())

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()
#> regr.mse 
#> 4.415262

Dictionary

Meta Information

Parameters

Custom mlr3 parameters

Offset

References

See also

Super classes

Methods

Public methods

LearnerRegrCVGlmnet$new()

Usage

LearnerRegrCVGlmnet$selected_features()

Usage

Arguments

Returns

LearnerRegrCVGlmnet$clone()

Usage

Arguments

Examples

`LearnerRegrCVGlmnet$new()`

`LearnerRegrCVGlmnet$selected_features()`

`LearnerRegrCVGlmnet$clone()`