default_gp: Default Gaussian Process

In mlr3mbo: Flexible Bayesian Optimization

Default Gaussian Process

Description

This is a helper function that constructs a default Gaussian Process mlr3learners::LearnerRegrKM which is for example used in default_surrogate().

Constructs a Kriging learner "regr.km" with kernel "matern5_2". If noisy = FALSE (default) a small nugget effect is added nugget.stability = 10^-8 to increase numerical stability to hopefully prevent crashes of DiceKriging. If noisy = TRUE the nugget effect will be estimated with nugget.estim = TRUE. If noisy = TRUE jitter is set to TRUE to circumvent a problem with DiceKriging where already trained input values produce the exact trained output. In general, instead of the default "BFGS" optimization method we use rgenoud ("gen"), which is a hybrid algorithm, to combine global search based on genetic algorithms and local search based on gradients. This may improve the model fit and will less frequently produce a constant model prediction.

Usage

default_gp(noisy = FALSE)

Arguments

noisy

(logical(1)) Whether the learner will be used in a noisy objective function scenario. See above.

Value

mlr3learners::LearnerRegrKM

See Also

Other mbo_defaults: default_acqfunction(), default_acqoptimizer(), default_loop_function(), default_result_assigner(), default_rf(), default_surrogate(), mbo_defaults

mlr3mbo documentation built on April 24, 2026, 5:07 p.m.