k-Nearest-Neighbor Regression Learner

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

Note

There is no training step for k-NN models, just storing the training data to process it during the predict step. Therefore, $model returns a list with the following elements:

• formula: Formula for calling kknn::kknn() during $predict().

• data: Training data for calling kknn::kknn() during $predict().

• pars: Training parameters for calling kknn::kknn() during $predict().

• kknn: Model as returned by kknn::kknn(), only available after $predict() has been called.

Dictionary

This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

mlr_learners$get("regr.kknn")
lrn("regr.kknn")

References

Cover, Thomas, Hart, Peter (1967). “Nearest neighbor pattern classification.” IEEE transactions on information theory, 13(1), 21--27. doi: 10.1109/TIT.1967.1053964 . Hechenbichler, Klaus, Schliep, Klaus (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, J R (2012). “Optimal weighted nearest neighbour classifiers.” The Annals of Statistics, 40(5), 2733--2763. doi: 10.1214/12-AOS1049 .

See also

Dictionary of Learners: mlr3::mlr_learners

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrKKNN

Methods

Public methods

• LearnerRegrKKNN$new()

• LearnerRegrKKNN$clone()

Inherited methods

• 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

LearnerRegrKKNN$new()

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Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerRegrKKNN$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

if (requireNamespace("kknn")) {
  learner = mlr3::lrn("regr.kknn")
  print(learner)

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