Regression Relevance Vector Machine Learner
mlr_learners_regr.rvm.Rd
Bayesian version of the support vector machine.
Parameters sigma
, degree
, scale
, offset
, order
, length
,
lambda
, and normalized
are added to make tuning kpar
easier.
If kpar
is provided then these new parameters are ignored. If none are
provided then the default "automatic" is used for kpar
.
Calls kernlab::rvm()
from package kernlab.
Dictionary
This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn()
:
$get("regr.rvm")
mlr_learnerslrn("regr.rvm")
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 |
kernel | character | rbfdot | rbfdot, polydot, vanilladot, tanhdot, laplacedot, besseldot, anovadot, splinedot, stringdot | - |
sigma | numeric | - | \((-\infty, \infty)\) | |
degree | numeric | - | \((-\infty, \infty)\) | |
scale | numeric | - | \((-\infty, \infty)\) | |
offset | numeric | - | \((-\infty, \infty)\) | |
order | numeric | - | \((-\infty, \infty)\) | |
length | integer | - | \([0, \infty)\) | |
lambda | numeric | - | \((-\infty, \infty)\) | |
normalized | logical | - | TRUE, FALSE | - |
kpar | untyped | automatic | - | |
alpha | untyped | 5 | - | |
var | numeric | 0.1 | \([0.001, \infty)\) | |
var.fix | logical | FALSE | TRUE, FALSE | - |
iterations | integer | 100 | \([0, \infty)\) | |
tol | numeric | 2.220446e-16 | \([0, \infty)\) | |
minmaxdiff | numeric | 0.001 | \([0, \infty)\) | |
verbosity | logical | FALSE | TRUE, FALSE | - |
fit | logical | TRUE | TRUE, FALSE | - |
na.action | untyped | na.omit | - |
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
-> LearnerRegrRVM
Examples
learner = mlr3::lrn("regr.rvm")
print(learner)
#> <LearnerRegrRVM:regr.rvm>: Relevance Vector Machine
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3extralearners, kernlab
#> * Predict Types: [response]
#> * Feature Types: logical, integer, numeric, character, factor, ordered
#> * Properties: -
# available parameters:
learner$param_set$ids()
#> [1] "kernel" "sigma" "degree" "scale" "offset"
#> [6] "order" "length" "lambda" "normalized" "kpar"
#> [11] "alpha" "var" "var.fix" "iterations" "tol"
#> [16] "minmaxdiff" "verbosity" "fit" "na.action"