Regression Kernlab Support Vector Machine
mlr_learners_regr.ksvm.Rd
Support Vector Regression.
Calls kernlab::ksvm()
from kernlab.
Dictionary
This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn()
:
$get("regr.ksvm")
mlr_learnerslrn("regr.ksvm")
Meta Information
Task type: “regr”
Predict Types: “response”
Feature Types: “logical”, “integer”, “numeric”, “character”, “factor”, “ordered”
Required Packages: mlr3, mlr3extralearners, kernlab
Parameters
Id | Type | Default | Levels | Range |
scaled | logical | TRUE | TRUE, FALSE | - |
type | character | eps-svr | eps-svr, nu-svr, eps-bsvr | - |
kernel | character | rbfdot | rbfdot, polydot, vanilladot, laplacedot, besseldot, anovadot | - |
C | numeric | 1 | \((-\infty, \infty)\) | |
nu | numeric | 0.2 | \([0, \infty)\) | |
epsilon | numeric | 0.1 | \((-\infty, \infty)\) | |
cache | integer | 40 | \([1, \infty)\) | |
tol | numeric | 0.001 | \([0, \infty)\) | |
shrinking | logical | TRUE | TRUE, FALSE | - |
sigma | numeric | - | \([0, \infty)\) | |
degree | integer | - | \([1, \infty)\) | |
scale | numeric | - | \([0, \infty)\) | |
order | integer | - | \((-\infty, \infty)\) | |
offset | numeric | - | \((-\infty, \infty)\) | |
na.action | untyped | na.omit | - | |
fit | logical | TRUE | TRUE, FALSE | - |
References
Karatzoglou, Alexandros, Smola, Alex, Hornik, Kurt, Zeileis, Achim (2004). “kernlab-an S4 package for kernel methods in R.” Journal of statistical software, 11(9), 1--20.
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
-> LearnerRegrKSVM
Examples
learner = mlr3::lrn("regr.ksvm")
print(learner)
#> <LearnerRegrKSVM:regr.ksvm>: Support Vector Machine
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3extralearners, kernlab
#> * Predict Types: [response]
#> * Feature Types: logical, integer, numeric, character, factor, ordered
#> * Properties: weights
# available parameters:
learner$param_set$ids()
#> [1] "scaled" "type" "kernel" "C" "nu" "epsilon"
#> [7] "cache" "tol" "shrinking" "sigma" "degree" "scale"
#> [13] "order" "offset" "na.action" "fit"