Classification Bayes Network Learner
mlr_learners_classif.bayes_net.Rd
Bayes Network learning using various search algorithms.
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
N
removed:Initial structure is empty
P
removed:Maximum number of parents
R
removed:Random order
mbc
removed:Applies a Markov Blanket correction to the network structure, after a network structure is learned
S
removed:Score type
A
removed:Initial count (alpha)
Reason for change: The parameters are removed because they don't work out of the box and it's unclear how to use them.
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 | - | - | |
D | logical | - | TRUE, FALSE | - |
B | untyped | - | - | |
Q | character | - | global.K2, global.HillClimber, global.SimulatedAnnealing, global.TabuSearch, global.TAN, local.K2, local.HillClimber, local.LAGDHillClimber, local.SimulatedAnnealing, local.TabuSearch, ... | - |
E | character | - | estimate.SimpleEstimator, estimate.BMAEstimator, estimate.MultiNomialBMAEstimator | - |
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
-> LearnerClassifBayesNet
Examples
learner = mlr3::lrn("classif.bayes_net")
print(learner)
#> <LearnerClassifBayesNet:classif.bayes_net>: Bayes Network
#> * 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] "D" "B"
#> [5] "Q" "E"
#> [7] "output_debug_info" "do_not_check_capabilities"
#> [9] "num_decimal_places" "batch_size"
#> [11] "options"