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.

Format

R6::R6Class() inheriting from mlr3::LearnerRegr.

Construction

LearnerRegrKM$new()
mlr3::mlr_learners$get("regr.km")
mlr3::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

Examples

learner = mlr3::lrn("regr.km") print(learner)
#> <LearnerRegrKM:regr.km> #> * Model: - #> * Parameters: list() #> * Packages: DiceKriging #> * Predict Type: response #> * Feature types: integer, numeric #> * Properties: -
# available parameters: learner$param_set$ids()
#> [1] "covtype" "nugget" "nugget.estim" "type" #> [5] "nugget.stability" "jitter"