Kriging Regression Learner

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

Dictionary of Learners: mlr3::mlr_learners

Super classes

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

Methods

Public methods

• LearnerRegrKM$new()

• LearnerRegrKM$clone()

Inherited methods

• mlr3::Learner$format()

• mlr3::Learner$help()

• mlr3::Learner$predict()

• mlr3::Learner$predict_newdata()

• mlr3::Learner$print()

• mlr3::Learner$reset()

• mlr3::Learner$train()

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Method new()

Creates a new instance of this R6 class.

Usage

LearnerRegrKM$new()

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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"           "control"          "se.compute"      
#> [25] "cov.compute"      "light.return"     "bias.correct"     "checkNames"