mlr_learners_classif.fnn | R Documentation |
Fast Nearest Neighbour Classification.
Calls FNN::knn()
from FNN.
This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn()
:
mlr_learners$get("classif.fnn") lrn("classif.fnn")
Task type: “classif”
Predict Types: “response”, “prob”
Feature Types: “integer”, “numeric”
Required Packages: mlr3, mlr3extralearners, FNN
Id | Type | Default | Levels | Range |
k | integer | 1 | [1, \infty) |
|
algorithm | character | kd_tree | kd_tree, cover_tree, brute | - |
mlr3::Learner
-> mlr3::LearnerClassif
-> LearnerClassifFNN
new()
Creates a new instance of this R6 class.
LearnerClassifFNN$new()
clone()
The objects of this class are cloneable with this method.
LearnerClassifFNN$clone(deep = FALSE)
deep
Whether to make a deep clone.
be-marc
Boltz, Sylvain, Debreuve, Eric, Barlaud, Michel (2007). “kNN-based high-dimensional Kullback-Leibler distance for tracking.” In Eighth International Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS'07), 16–16. IEEE.
Dictionary of Learners: mlr3::mlr_learners.
as.data.table(mlr_learners)
for a table of available Learners in the running session (depending on the loaded packages).
Chapter in the mlr3book: https://mlr3book.mlr-org.com/basics.html#learners
mlr3learners for a selection of recommended learners.
mlr3cluster for unsupervised clustering learners.
mlr3pipelines to combine learners with pre- and postprocessing steps.
mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.
learner = mlr3::lrn("classif.fnn")
print(learner)
# available parameters:
learner$param_set$ids()
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