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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 “""” 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.


default_gp(noisy = FALSE)



Whether the learner will be used in a noisy objective function scenario. See above.