Regression Gaussian Processes Learner From Weka
mlr_learners_regr.gaussian_processes.Rd
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.
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 | - |
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
-> LearnerRegrGaussianProcesses
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$model)
#> Gaussian Processes
#>
#> Kernel used:
#> Linear Kernel: K(x,y) = <x,y>
#>
#> All values shown based on: Normalize training data
#>
#> Average Target Value : 0.41320346320346313
#> Inverted Covariance Matrix:
#> Lowest Value = -0.20241986313181812
#> Highest Value = 0.8818171662233893
#> Inverted Covariance Matrix * Target-value Vector:
#> Lowest Value = -0.19608287233043026
#> Highest Value = 0.2274237690141056
#>
#>
# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)
# Score the predictions
predictions$score()
#> regr.mse
#> 7.563391