Classification MultilayerPerceptron Learner
mlr_learners_classif.multilayer_perceptron.Rd
Classifier that uses backpropagation to learn a multi-layer perceptron.
Calls RWeka::make_Weka_classifier()
from RWeka.
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
G
removed:GUI will be opened
Reason for change: The parameter is removed because we don't want to launch GUI.
Dictionary
This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn()
:
Parameters
Id | Type | Default | Levels | Range |
subset | untyped | - | - | |
na.action | untyped | - | - | |
L | numeric | 0.3 | \([0, 1]\) | |
M | numeric | 0.2 | \([0, 1]\) | |
N | integer | 500 | \([1, \infty)\) | |
V | numeric | 0 | \([0, 100]\) | |
S | integer | 0 | \([0, \infty)\) | |
E | integer | 20 | \([1, \infty)\) | |
A | logical | FALSE | TRUE, FALSE | - |
B | logical | FALSE | TRUE, FALSE | - |
H | untyped | "a" | - | |
C | logical | FALSE | TRUE, FALSE | - |
I | logical | FALSE | TRUE, FALSE | - |
R | logical | FALSE | TRUE, FALSE | - |
D | logical | FALSE | TRUE, FALSE | - |
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 | - |
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::LearnerClassif
-> LearnerClassifMultilayerPerceptron
Examples
learner = mlr3::lrn("classif.multilayer_perceptron")
print(learner)
#> <LearnerClassifMultilayerPerceptron:classif.multilayer_perceptron>: MultilayerPerceptron
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, RWeka
#> * Predict Types: [response], prob
#> * Feature Types: logical, integer, numeric, factor, ordered
#> * Properties: missings, multiclass, twoclass
# available parameters:
learner$param_set$ids()
#> [1] "subset" "na.action"
#> [3] "L" "M"
#> [5] "N" "V"
#> [7] "S" "E"
#> [9] "A" "B"
#> [11] "H" "C"
#> [13] "I" "R"
#> [15] "D" "output_debug_info"
#> [17] "do_not_check_capabilities" "num_decimal_places"
#> [19] "batch_size" "options"