mlr_learners_classif.gausspr: Classification Gaussian Process Learner

mlr_learners_classif.gaussprR Documentation

Classification Gaussian Process Learner

Description

Gaussian process for classification. Calls kernlab::gausspr() from kernlab. Parameters sigma, degree, scale, offset and order 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.

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.gausspr")

Meta Information

  • Task type: “classif”

  • Predict Types: “response”, “prob”

  • Feature Types: “logical”, “integer”, “numeric”, “character”, “factor”, “ordered”

  • Required Packages: mlr3, mlr3extralearners, kernlab

Parameters

Id Type Default Levels Range
scaled untyped TRUE -
kernel character rbfdot rbfdot, polydot, vanilladot, tanhdot, laplacedot, besseldot, anovadot, splinedot -
sigma numeric - (-\infty, \infty)
degree numeric - (-\infty, \infty)
scale numeric - (-\infty, \infty)
offset numeric - (-\infty, \infty)
order numeric - (-\infty, \infty)
kpar untyped "automatic" -
tol numeric 0.001 [0, \infty)
fit logical TRUE TRUE, FALSE -
na.action untyped na.omit -
coupler character minpair minpair, pkpd -

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifGausspr

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage
LearnerClassifGausspr$new()

Method clone()

The objects of this class are cloneable with this method.

Usage
LearnerClassifGausspr$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("classif.gausspr")
print(learner)

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

# 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.