mlr3learners (version 0.5.3)

mlr_learners_regr.km: Kriging Regression Learner

Description

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 | | |- |

Super classes

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

Methods

Public methods

• LearnerRegrKM$new()

• LearnerRegrKM$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$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.

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. tools:::Rd_expr_doi("10.18637/jss.v051.i01").

See Also

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/basics.html#learners

• Package mlr3extralearners for more learners.

• Dictionary of Learners: mlr_learners

• as.data.table(mlr_learners) for a table of available Learners in the running session (depending on the loaded packages).

• mlr3pipelines to combine learners with pre- and postprocessing steps.

• Extension packages for additional task types:

  • mlr3proba for probabilistic supervised regression and survival analysis.

  • mlr3cluster for unsupervised clustering.

• mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.

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

Examples

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

  # available parameters:
learner$param_set$ids()
}