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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")

Meta Information

, * Task type: “regr”, * Predict Types: “response”, “se”, * Feature Types: “logical”, “integer”, “numeric”, * Required Packages: mlr3, mlr3learners, DiceKriging

Parameters

, |Id |Type |Default |Levels |Range |, |:----------------|:---------|:---------|:----------------------------------------|:------------------------------------|, |bias.correct |logical |FALSE |TRUE, FALSE |- |, |checkNames |logical |TRUE |TRUE, FALSE |- |, |coef.cov |untyped | | |- |, |coef.trend |untyped | | |- |, |coef.var |untyped | | |- |, |control |untyped | | |- |, |cov.compute |logical |TRUE |TRUE, FALSE |- |, |covtype |character |matern5_2 |gauss, matern5_2, matern3_2, exp, powexp |- |, |estim.method |character |MLE |MLE, LOO |- |, |gr |logical |TRUE |TRUE, FALSE |- |, |iso |logical |FALSE |TRUE, FALSE |- |, |jitter |numeric |0 | |\([0, \infty)\) |, |kernel |untyped | | |- |, |knots |untyped | | |- |, |light.return |logical |FALSE |TRUE, FALSE |- |, |lower |untyped | | |- |, |multistart |integer |1 | |\((-\infty, \infty)\) |, |noise.var |untyped | | |- |, |nugget |numeric |- | |\((-\infty, \infty)\) |, |nugget.estim |logical |FALSE |TRUE, FALSE |- |, |nugget.stability |numeric |0 | |\([0, \infty)\) |, |optim.method |character |BFGS |BFGS, gen |- |, |parinit |untyped | | |- |, |penalty |untyped | | |- |, |scaling |logical |FALSE |TRUE, FALSE |- |, |se.compute |logical |TRUE |TRUE, FALSE |- |, |type |character |SK |SK, UK |- |, |upper |untyped | | |- |

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

Other Learner: mlr_learners_classif.cv_glmnet, mlr_learners_classif.glmnet, mlr_learners_classif.kknn, mlr_learners_classif.lda, mlr_learners_classif.log_reg, mlr_learners_classif.multinom, mlr_learners_classif.naive_bayes, mlr_learners_classif.nnet, mlr_learners_classif.qda, mlr_learners_classif.ranger, mlr_learners_classif.svm, mlr_learners_classif.xgboost, mlr_learners_regr.cv_glmnet, mlr_learners_regr.glmnet, mlr_learners_regr.kknn, mlr_learners_regr.lm, mlr_learners_regr.ranger, mlr_learners_regr.svm, mlr_learners_regr.xgboost

Super classes

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

Methods

Inherited methods


Method new()

Creates a new instance of this R6 class.

Usage


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", quietly = TRUE)) {
  learner = mlr3::lrn("regr.km")
  print(learner)

  # available parameters:
learner$param_set$ids()
}
#> <LearnerRegrKM:regr.km>
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3learners, DiceKriging
#> * Predict Type: response
#> * Feature types: logical, integer, numeric
#> * Properties: -
#>  [1] "bias.correct"     "checkNames"       "coef.cov"         "coef.trend"      
#>  [5] "coef.var"         "control"          "cov.compute"      "covtype"         
#>  [9] "estim.method"     "gr"               "iso"              "jitter"          
#> [13] "kernel"           "knots"            "light.return"     "lower"           
#> [17] "multistart"       "noise.var"        "nugget"           "nugget.estim"    
#> [21] "nugget.stability" "optim.method"     "parinit"          "penalty"         
#> [25] "scaling"          "se.compute"       "type"             "upper"