This is a helper function that constructs a default Gaussian Process mlr3::LearnerRegr 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.
Arguments
- noisy
(logical(1))
Whether the learner will be used in a noisy objective function scenario. See above.
See also
Other mbo_defaults:
default_acqfunction(),
default_acqoptimizer(),
default_loop_function(),
default_result_assigner(),
default_rf(),
default_surrogate(),
mbo_defaults