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 hyperparameternugget
withnugget.stability * var(y)
before training to improve the numerical stability. We recommend a value of1e-8
.The additional hyperparameter
jitter
can be set to addN(0, [jitter])
-distributed noise to the data before prediction to avoid perfect interpolation. We recommend a value of1e-12
.
Dictionary
This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn()
:
$get("regr.km")
mlr_learnerslrn("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
Chapter in the mlr3book: https://mlr3book.mlr-org.com/chapters/chapter2/data_and_basic_modeling.html#sec-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.nnet
,
mlr_learners_regr.ranger
,
mlr_learners_regr.svm
,
mlr_learners_regr.xgboost
Super classes
mlr3::Learner
-> mlr3::LearnerRegr
-> LearnerRegrKM
Examples
if (requireNamespace("DiceKriging", quietly = TRUE)) {
learner = mlr3::lrn("regr.km")
print(learner)
# available parameters:
learner$param_set$ids()
}
#> <LearnerRegrKM:regr.km>: Kriging
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3learners, DiceKriging
#> * Predict Types: [response], se
#> * 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"