Classification Gaussian Process Learner

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	-

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

• Dictionary of Learners: mlr3::mlr_learners.

• 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.

Author

RaphaelS1

Super classes

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

Methods

Public methods

• LearnerClassifGausspr$new()

• LearnerClassifGausspr$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$encapsulate()
• mlr3::Learner$format()
• mlr3::Learner$help()
• mlr3::Learner$predict()
• mlr3::Learner$predict_newdata()
• mlr3::Learner$print()
• mlr3::Learner$reset()
• mlr3::Learner$train()

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Method new()

Creates a new instance of this R6 class.

Usage

LearnerClassifGausspr$new()

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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.

Examples

# Define the Learner
learner = mlr3::lrn("classif.gausspr")
print(learner)
#> <LearnerClassifGausspr:classif.gausspr>: Gaussian Process
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3extralearners, kernlab
#> * Predict Types:  [response], prob
#> * Feature Types: logical, integer, numeric, character, factor, ordered
#> * Properties: multiclass, twoclass

# 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)
#> Using automatic sigma estimation (sigest) for RBF or laplace kernel 

print(learner$model)
#> Gaussian Processes object of class "gausspr" 
#> Problem type: classification 
#> 
#> Gaussian Radial Basis kernel function. 
#>  Hyperparameter : sigma =  0.0119510962717118 
#> 
#> Number of training instances learned : 139 
#> Train error : 0.079136691 


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

# Score the predictions
predictions$score()
#> classif.ce 
#>   0.115942