mlr_learners_regr.km: Kriging Regression Learner

mlr_learners_regr.kmR Documentation

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 mlr3::Learner can be instantiated via the dictionary mlr3::mlr_learners or with the associated sugar function mlr3::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 NULL -
coef.trend untyped NULL -
coef.var untyped NULL -
control untyped NULL -
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 NULL -
knots untyped NULL -
light.return logical FALSE TRUE, FALSE -
lower untyped NULL -
multistart integer 1 (-\infty, \infty)
noise.var untyped NULL -
nugget numeric - (-\infty, \infty)
nugget.estim logical FALSE TRUE, FALSE -
nugget.stability numeric 0 [0, \infty)
optim.method character BFGS BFGS, gen -
parinit untyped NULL -
penalty untyped NULL -
scaling logical FALSE TRUE, FALSE -
se.compute logical TRUE TRUE, FALSE -
type character SK SK, UK -
upper untyped NULL -

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.

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. \Sexpr[results=rd]{tools:::Rd_expr_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.nnet, mlr_learners_regr.ranger, mlr_learners_regr.svm, mlr_learners_regr.xgboost

Examples

if (requireNamespace("DiceKriging", quietly = TRUE)) {
# Define the Learner and set parameter values
learner = lrn("regr.km")
print(learner)

# Define a Task
task = tsk("mtcars")

# Create train and test set
ids = partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

# print the model
print(learner$model)

# importance method
if("importance" %in% learner$properties) print(learner$importance)

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()
}

mlr3learners documentation built on June 28, 2024, 5:09 p.m.