Classification Abess Learner

Adaptive best-subset selection for classification. Calls abess::abess() from abess.

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.abess")

Meta Information

• Task type: “classif”

• Predict Types: “response”, “prob”

• Feature Types: “integer”, “numeric”

• Required Packages: mlr3, abess

Parameters

Id	Type	Default	Levels	Range
family	character	-	binomial, multinomial, ordinal	-
tune.path	character	sequence	sequence, gsection	-
tune.type	character	gic	gic, aic, bic, ebic, cv	-
normalize	integer	NULL		\((-\infty, \infty)\)
support.size	untyped	NULL		-
c.max	integer	2		\([1, \infty)\)
gs.range	untyped	NULL		-
lambda	numeric	0		\([0, \infty)\)
always.include	untyped	NULL		-
group.index	untyped	NULL		-
init.active.set	untyped	NULL		-
splicing.type	integer	2		\([1, 2]\)
max.splicing.iter	integer	20		\([1, \infty)\)
screening.num	integer	NULL		\([0, \infty)\)
important.search	integer	NULL		\([0, \infty)\)
warm.start	logical	TRUE	TRUE, FALSE	-
nfolds	integer	5		\((-\infty, \infty)\)
foldid	untyped	NULL		-
cov.update	logical	FALSE	TRUE, FALSE	-
newton	character	exact	exact, approx	-
newton.thresh	numeric	1e-06		\([0, \infty)\)
max.newton.iter	integer	NULL		\([1, \infty)\)
early.stop	logical	FALSE	TRUE, FALSE	-
ic.scale	numeric	1		\([0, \infty)\)
num.threads	integer	0		\([0, \infty)\)
seed	integer	0		\((-\infty, \infty)\)

Initial parameter values

• num.threads: This parameter is initialized to 1 (default is 0) to avoid conflicts with the mlr3 parallelization.

• family: Depends on the task type, if the parameter family is NULL, it is set to "binomial" for binary classification tasks and to "multinomial" for multiclass classification problems.

See also

• Dictionary of Learners: mlr3::mlr_learners.

• as.data.table(mlr_learners) for a table of available Learners in the running session (depending on the loaded packages).

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/basics.html#learners

• mlr3learners for a selection of recommended learners.

• mlr3cluster for unsupervised clustering learners.

• mlr3pipelines to combine learners with pre- and postprocessing steps.

• mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.

Author

abess-team

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifAbess

Methods

Public methods

• LearnerClassifAbess$new()

• LearnerClassifAbess$selected_features()

• LearnerClassifAbess$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$encapsulate()
• 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

LearnerClassifAbess$new()

---

Method selected_features()

Extract the name of selected features from the model by abess::extract().

Usage

LearnerClassifAbess$selected_features()

Returns

The names of selected features

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifAbess$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner
learner = mlr3::lrn("classif.abess")
print(learner)
#> <LearnerClassifAbess:classif.abess>: Fast Best Subset Selection for Classification
#> * Model: -
#> * Parameters: num.threads=1
#> * Packages: mlr3, abess
#> * Predict Types:  [response], prob
#> * Feature Types: integer, numeric
#> * Properties: multiclass, selected_features, twoclass, weights

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)
#> Call:
#> abess.default(x = x, y = y, family = "binomial", num.threads = 1L)
#> 
#>    support.size      dev      GIC
#> 1             0 96.31508 192.6302
#> 2             1 77.77817 162.0919
#> 3             2 71.52497 156.1211
#> 4             3 64.58873 148.7842
#> 5             4 60.32146 146.7852
#> 6             5 57.22205 147.1220
#> 7             6 54.25590 147.7253
#> 8             7 50.42646 146.6020
#> 9             8 47.21606 146.7168
#> 10            9 44.17528 147.1708
#> 11           10 39.75582 144.8674
#> 12           11 37.07486 146.0411
#> 13           12 35.28734 149.0017
#> 14           13 34.09136 153.1453
#> 15           14 32.93729 157.3727
#> 16           15 31.79613 161.6260
#> 17           16 30.60256 165.7744
#> 18           17 21.79038 154.6857
#> 19           18 18.43008 154.5006
#> 20           19 15.67258 155.5212
#> 21           20 13.77575 158.2631
#> 22           21 12.02102 161.2892


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

# Score the predictions
predictions$score()
#> classif.ce 
#>  0.2753623