Skip to contents

Fast Nearest Neighbour Regression. Calls FNN::knn.reg() from FNN.

Dictionary

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

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

Meta Information

  • Task type: “regr”

  • Predict Types: “response”

  • Feature Types: “integer”, “numeric”

  • Required Packages: mlr3, mlr3extralearners, FNN

Parameters

IdTypeDefaultLevelsRange
kinteger1\([1, \infty)\)
algorithmcharacterkd_treekd_tree, cover_tree, brute-

References

Boltz, Sylvain, Debreuve, Eric, Barlaud, Michel (2007). “kNN-based high-dimensional Kullback-Leibler distance for tracking.” In Eighth International Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS'07), 16--16. IEEE.

See also

Author

be-marc

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrFNN

Methods

Inherited methods


Method new()

Creates a new instance of this R6 class.

Usage


Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerRegrFNN$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

learner = mlr3::lrn("regr.fnn")
print(learner)
#> <LearnerRegrFNN:regr.fnn>: Fast Nearest Neighbour
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3extralearners, FNN
#> * Predict Type: response
#> * Feature types: integer, numeric
#> * Properties: -

# available parameters:
learner$param_set$ids()
#> [1] "k"         "algorithm"