Gaussian Process Regression Learner

Gaussian Process regression using the GPfit package. Calls GPfit::GP_fit() for training and predict method for predictions.

References

MacDonald, B., Ranjan, P., Chipman, H. (2015). "GPfit: An R Package for Fitting a Gaussian Process Model to Deterministic Simulator Outputs." Journal of Statistical Software, 64(12), 1-23.

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrGPfit

Methods

Public methods

• LearnerRegrGPfit$new()

• LearnerRegrGPfit$clone()

Inherited methods

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

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Method new()

Creates a new instance of this R6 class.

Usage

LearnerRegrGPfit$new()

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Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerRegrGPfit$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

library(mlr3)

# Create a regression task
task = tsk("mtcars")

# Create the learner
learner = lrn("regr.gpfit")

# Train the model
learner$train(task)
#> Warning: X should be in range (0, 1)

# Make predictions
prediction = learner$predict(task)
print(prediction)
#> 
#> ── <PredictionRegr> for 32 observations: ───────────────────────────────────────
#>  row_ids truth response
#>        1  21.0     21.0
#>        2  21.0     21.0
#>        3  22.8     22.8
#>      ---   ---      ---
#>       30  19.7     19.7
#>       31  15.0     15.0
#>       32  21.4     21.4