Regression Gaussian Processes Learner From Weka

Gaussian Processes. Calls RWeka::make_Weka_classifier() from RWeka.

Custom mlr3 parameters

• output_debug_info:

  • original id: output-debug-info

• do_not_check_capabilities:

  • original id: do-not-check-capabilities

• num_decimal_places:

  • original id: num-decimal-places

• batch_size:

  • original id: batch-size

• E_poly:

  • original id: E

• L_poly:

  • original id: L (duplicated L for when K is set to PolyKernel)

• C_poly:

  • original id: C

• Reason for change: This learner contains changed ids of the following control arguments since their ids contain irregular pattern

• output-debug-info for kernel parameter removed:

  • enables debugging output (if available) to be printed

• Reason for change: The parameter is removed because it's unclear how to actually use it.

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.gaussian_processes")

Meta Information

• Task type: “regr”

• Predict Types: “response”

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

• Required Packages: mlr3, RWeka

Parameters

Id	Type	Default	Levels	Range
subset	untyped	-		-
na.action	untyped	-		-
L	numeric	1		\((-\infty, \infty)\)
N	character	0	0, 1, 2	-
K	character	supportVector.PolyKernel	supportVector.NormalizedPolyKernel, supportVector.PolyKernel, supportVector.Puk, supportVector.RBFKernel, supportVector.StringKernel	-
S	integer	1		\((-\infty, \infty)\)
E_poly	numeric	1		\((-\infty, \infty)\)
L_poly	logical	FALSE	TRUE, FALSE	-
C_poly	integer	250007		\((-\infty, \infty)\)
output_debug_info	logical	FALSE	TRUE, FALSE	-
do_not_check_capabilities	logical	FALSE	TRUE, FALSE	-
num_decimal_places	integer	2		\([1, \infty)\)
batch_size	integer	100		\([1, \infty)\)
options	untyped	NULL		-

References

Mackay DJ (1998). “Introduction to Gaussian Processes.”

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

damirpolat

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrGaussianProcesses

Methods

Public methods

• LearnerRegrGaussianProcesses$new()

• LearnerRegrGaussianProcesses$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()

---

Method new()

Creates a new instance of this R6 class.

Usage

LearnerRegrGaussianProcesses$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerRegrGaussianProcesses$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner
learner = mlr3::lrn("regr.gaussian_processes")
print(learner)
#> <LearnerRegrGaussianProcesses:regr.gaussian_processes>: Gaussian Processes
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, RWeka
#> * Predict Types:  [response]
#> * Feature Types: logical, integer, numeric, factor, ordered
#> * Properties: missings

# 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)
#> Gaussian Processes
#> 
#> Kernel used:
#>   Linear Kernel: K(x,y) = <x,y>
#> 
#> All values shown based on: Normalize training data
#> 
#> Average Target Value : 0.3773049645390071
#> Inverted Covariance Matrix:
#>     Lowest Value = -0.3056004263483811
#>     Highest Value = 0.8999925958808928
#> Inverted Covariance Matrix * Target-value Vector:
#>     Lowest Value = -0.1543654033592824
#>     Highest Value = 0.24935789899276486
#>  
#> 


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

# Score the predictions
predictions$score()
#> regr.mse 
#> 8.805292