LiblineaR Classification Learner
mlr_learners_classif.liblinear.Rd
L2 regularized support vector classification.
Calls LiblineaR::LiblineaR()
from LiblineaR.
Details
Type of SVC depends on type
argument:
0
– L2-regularized logistic regression (primal)1
- L2-regularized L2-loss support vector classification (dual)3
- L2-regularized L1-loss support vector classification (dual)2
– L2-regularized L2-loss support vector classification (primal)4
– Support vector classification by Crammer and Singer5
- L1-regularized L2-loss support vector classification6
- L1-regularized logistic regression7
- L2-regularized logistic regression (dual)
If number of records > number of features, type = 2
is faster than type = 1
(Hsu et al. 2003).
Note that probabilistic predictions are only available for types 0
, 6
, and 7
.
The default epsilon
value depends on the type
parameter, see LiblineaR::LiblineaR.
Dictionary
This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn()
:
$get("classif.liblinear")
mlr_learnerslrn("classif.liblinear")
Meta Information
Task type: “classif”
Predict Types: “response”, “prob”
Feature Types: “numeric”
Required Packages: mlr3, mlr3extralearners, LiblineaR
Parameters
Id | Type | Default | Levels | Range |
type | integer | 0 | \([0, 7]\) | |
cost | numeric | 1 | \([0, \infty)\) | |
epsilon | numeric | - | \([0, \infty)\) | |
bias | numeric | 1 | \((-\infty, \infty)\) | |
cross | integer | 0 | \([0, \infty)\) | |
verbose | logical | FALSE | TRUE, FALSE | - |
wi | untyped | - | ||
findC | logical | FALSE | TRUE, FALSE | - |
useInitC | logical | TRUE | TRUE, FALSE | - |
References
Fan, Rong-En, Chang, Kai-Wei, Hsieh, Cho-Jui, Wang, Xiang-Rui, Lin, Chih-Jen (2008). “LIBLINEAR: A library for large linear classification.” the Journal of machine Learning research, 9, 1871--1874.
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::LearnerClassif
-> LearnerClassifLiblineaR
Examples
learner = mlr3::lrn("classif.liblinear")
print(learner)
#> <LearnerClassifLiblineaR:classif.liblinear>: Support Vector Machine
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3extralearners, LiblineaR
#> * Predict Types: [response], prob
#> * Feature Types: numeric
#> * Properties: multiclass, twoclass
# available parameters:
learner$param_set$ids()
#> [1] "type" "cost" "epsilon" "bias" "cross" "verbose" "wi"
#> [8] "findC" "useInitC"