Regression Abess Learner
mlr_learners_regr.abess.Rd
Adaptive best-subset selection for regression.
Calls abess::abess()
from abess.
Dictionary
This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn()
:
$get("regr.abess")
mlr_learnerslrn("regr.abess")
Parameters
Id | Type | Default | Levels | Range |
family | character | gaussian | gaussian, mgaussian, poisson, gamma | - |
tune.path | character | sequence | sequence, gsection | - |
tune.type | character | gic | gic, aic, bic, ebic, cv | - |
normalize | integer | NULL | \((-\infty, \infty)\) | |
support.size | untyped | - | ||
c.max | integer | 2 | \([1, \infty)\) | |
gs.range | untyped | - | ||
lambda | numeric | 0 | \([0, \infty)\) | |
always.include | untyped | - | ||
group.index | untyped | - | ||
init.active.set | untyped | - | ||
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 | - | ||
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 | 1 | \((-\infty, \infty)\) |
Initial parameter values
num.threads
: This parameter is initialized to 1 (default is 0) to avoid conflicts with the mlr3 parallelization.
See also
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.
Super classes
mlr3::Learner
-> mlr3::LearnerRegr
-> LearnerRegrAbess
Methods
Method selected_features()
Extract the name of selected features from the model by abess::extract()
.
Examples
learner = mlr3::lrn("regr.abess")
print(learner)
#> <LearnerRegrAbess:regr.abess>: Fast Best Subset Selection for Regression
#> * Model: -
#> * Parameters: num.threads=1
#> * Packages: mlr3, abess
#> * Predict Types: [response]
#> * Feature Types: integer, numeric
#> * Properties: selected_features, weights
# available parameters:
learner$param_set$ids()
#> [1] "family" "tune.path" "tune.type"
#> [4] "normalize" "support.size" "c.max"
#> [7] "gs.range" "lambda" "always.include"
#> [10] "group.index" "init.active.set" "splicing.type"
#> [13] "max.splicing.iter" "screening.num" "important.search"
#> [16] "warm.start" "nfolds" "foldid"
#> [19] "cov.update" "newton" "newton.thresh"
#> [22] "max.newton.iter" "early.stop" "ic.scale"
#> [25] "num.threads" "seed"