Regression Fast Nearest Neighbor Search Learner
mlr_learners_regr.fnn.Rd
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()
:
Meta Information
Task type: “regr”
Predict Types: “response”
Feature Types: “integer”, “numeric”
Required Packages: mlr3, mlr3extralearners, FNN
Parameters
Id | Type | Default | Levels | Range |
k | integer | 1 | \([1, \infty)\) | |
algorithm | character | kd_tree | kd_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
as.data.table(mlr_learners)
for a table of available Learners in the running session (depending on the loaded packages).Chapter in the mlr3book: https://mlr3book.mlr-org.com/basics.html#learners
mlr3learners for a selection of recommended learners.
mlr3cluster for unsupervised clustering learners.
mlr3pipelines to combine learners with pre- and postprocessing steps.
mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.
Super classes
mlr3::Learner
-> mlr3::LearnerRegr
-> LearnerRegrFNN
Examples
learner = mlr3::lrn("regr.fnn")
print(learner)
#> <LearnerRegrFNN:regr.fnn>: Fast Nearest Neighbour
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3extralearners, FNN
#> * Predict Types: [response]
#> * Feature Types: integer, numeric
#> * Properties: -
# available parameters:
learner$param_set$ids()
#> [1] "k" "algorithm"