mlr_learners_regr.rvm: Regression Relevance Vector Machine Learner

mlr_learners_regr.rvmR Documentation

Regression Relevance Vector Machine Learner

Description

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 lrn():

lrn("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 -

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrRVM

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage
LearnerRegrRVM$new()

Method clone()

The objects of this class are cloneable with this method.

Usage
LearnerRegrRVM$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

Author(s)

RaphaelS1

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

Examples


# Define the Learner
learner = mlr3::lrn("regr.rvm")
print(learner)

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

# 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)


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

# Score the predictions
predictions$score()


mlr-org/mlr3extralearners documentation built on Dec. 21, 2024, 2:21 p.m.