Kriging regression. Calls DiceKriging::km() from package DiceKriging.

  • The predict type hyperparameter "type" defaults to "SK" (simple Kriging).

  • The additional hyperparameter nugget.stability is used to overwrite the hyperparameter nugget with nugget.stability * var(y) before training to improve the numerical stability. We recommend a value of 1e-8.

  • The additional hyperparameter jitter can be set to add N(0, [jitter])-distributed noise to the data before prediction to avoid perfect interpolation. We recommend a value of 1e-12.

Dictionary

This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

mlr_learners$get("regr.km")
lrn("regr.km")

References

Roustant O, Ginsbourger D, Deville Y (2012). “DiceKriging, DiceOptim: Two R Packages for the Analysis of Computer Experiments by Kriging-Based Metamodeling and Optimization.” Journal of Statistical Software, 51(1), 1--55. doi: 10.18637/jss.v051.i01 .

See also

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrKM

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage

LearnerRegrKM$new()


Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerRegrKM$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

if (requireNamespace("DiceKriging")) { learner = mlr3::lrn("regr.km") print(learner) # available parameters: learner$param_set$ids() }
#> Loading required namespace: DiceKriging
#> <LearnerRegrKM:regr.km> #> * Model: - #> * Parameters: list() #> * Packages: DiceKriging #> * Predict Type: response #> * Feature types: logical, integer, numeric #> * Properties: -
#> [1] "covtype" "coef.trend" "coef.cov" "coef.var" #> [5] "nugget" "nugget.estim" "nugget.stability" "noise.var" #> [9] "estim.method" "penalty" "optim.method" "parinit" #> [13] "multistart" "lower" "upper" "gr" #> [17] "iso" "scaling" "knots" "kernel" #> [21] "type" "jitter"