k-Nearest-Neighbor regression. Calls kknn::kknn() from package kknn.

Format

R6::R6Class() inheriting from mlr3::LearnerRegr.

Construction

LearnerRegrKKNN$new()
mlr3::mlr_learners$get("regr.kknn")
mlr3::lrn("regr.kknn")

References

Hechenbichler K, Schliep K (2004). “Weighted k-nearest-neighbor techniques and ordinal classification.” Technical Report Discussion Paper 399, SFB 386, Ludwig-Maximilians University Munich. doi: 10.5282/ubm/epub.1769 .

Samworth RJ (2012). “Optimal weighted nearest neighbour classifiers.” The Annals of Statistics, 40(5), 2733--2763. doi: 10.1214/12-AOS1049 .

Cover T, Hart P (1967). “Nearest neighbor pattern classification.” IEEE transactions on information theory, 13(1), 21--27. doi: 10.1109/TIT.1967.1053964 .

See also

Examples

learner = mlr3::lrn("regr.kknn") print(learner)
#> <LearnerRegrKKNN:regr.kknn> #> * Model: - #> * Parameters: list() #> * Packages: withr, kknn #> * Predict Type: response #> * Feature types: logical, integer, numeric, factor, ordered #> * Properties: -
# available parameters: learner$param_set$ids()
#> [1] "k" "distance" "kernel" "scale"