Regression IBk Learner
mlr_learners_regr.IBk.Rd
Instance based algorithm: K-nearest neighbours regression.
Calls RWeka::IBk()
from RWeka.
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”, “factor”, “ordered”
Required Packages: mlr3, mlr3extralearners, RWeka
Parameters
Id | Type | Default | Levels | Range |
subset | untyped | - | - | |
na.action | untyped | - | - | |
weight | character | - | I, F | - |
K | integer | 1 | \([1, \infty)\) | |
E | logical | FALSE | TRUE, FALSE | - |
W | integer | 0 | \([0, \infty)\) | |
X | logical | FALSE | TRUE, FALSE | - |
A | character | LinearNNSearch | BallTree, CoverTree, FilteredNeighbourSearch, KDTree, LinearNNSearch | - |
output_debug_info | logical | FALSE | TRUE, FALSE | - |
do_not_check_capabilities | logical | FALSE | TRUE, FALSE | - |
num_decimal_places | integer | 2 | \([1, \infty)\) | |
batch_size | integer | 100 | \([1, \infty)\) | |
options | untyped | NULL | - |
Custom mlr3 parameters
output_debug_info
:original id: output-debug-info
do_not_check_capabilities
:original id: do-not-check-capabilities
num_decimal_places
:original id: num-decimal-places
batch_size
:original id: batch-size
Reason for change: This learner contains changed ids of the following control arguments since their ids contain irregular pattern
weight
:original id: I and F
Reason for change: original
I
andF
params are interdependent (I
can only beTRUE
whenF
isFALSE
and vice versa). The easiest way to encode this is to combineI
andF
into one factor param.
References
Aha, W D, Kibler, Dennis, Albert, K M (1991). “Instance-based learning algorithms.” Machine learning, 6(1), 37--66.
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
-> LearnerRegrIBk
Examples
learner = mlr3::lrn("regr.IBk")
print(learner)
#> <LearnerRegrIBk:regr.IBk>: K-nearest neighbour
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3extralearners, RWeka
#> * Predict Types: [response]
#> * Feature Types: integer, numeric, factor, ordered
#> * Properties: -
# available parameters:
learner$param_set$ids()
#> [1] "subset" "na.action"
#> [3] "weight" "K"
#> [5] "E" "W"
#> [7] "X" "A"
#> [9] "output_debug_info" "do_not_check_capabilities"
#> [11] "num_decimal_places" "batch_size"
#> [13] "options"