Classification Decision Table Learner
mlr_learners_classif.decision_table.Rd
Simple Decision Table majority classifier.
Calls RWeka::make_Weka_classifier()
from RWeka.
Initial parameter values
E
:Has only 2 out of 4 original evaluation measures : acc and auc with acc being the default
Reason for change: this learner should only contain evaluation measures appropriate for classification tasks
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
P_best
:original id: P
D_best
:original id: D
N_best
:original id: N
S_best
:original id: S
Reason for change: This learner contains changed ids of the following control arguments since their ids contain irregular pattern
Parameters
Id | Type | Default | Levels | Range |
subset | untyped | - | - | |
na.action | untyped | - | - | |
S | character | BestFirst | BestFirst, GreedyStepwise | - |
X | integer | 1 | \((-\infty, \infty)\) | |
E | character | acc | acc, auc | - |
I | logical | - | TRUE, FALSE | - |
R | logical | - | 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 | \([1, \infty)\) | |
P_best | untyped | - | - | |
D_best | character | 1 | 0, 1, 2 | - |
N_best | integer | - | \((-\infty, \infty)\) | |
S_best | integer | 1 | \((-\infty, \infty)\) | |
options | untyped | NULL | - |
References
Kohavi R (1995). “The Power of Decision Tables.” In 8th European Conference on Machine Learning, 174–189.
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
-> LearnerClassifDecisionTable
Examples
# Define the Learner
learner = mlr3::lrn("classif.decision_table")
print(learner)
#> <LearnerClassifDecisionTable:classif.decision_table>: Decision Table
#> * Model: -
#> * Parameters: E=acc
#> * Packages: mlr3, RWeka
#> * Predict Types: [response], prob
#> * Feature Types: logical, integer, numeric, factor, ordered
#> * Properties: missings, multiclass, twoclass
# Define a Task
task = mlr3::tsk("sonar")
# Create train and test set
ids = mlr3::partition(task)
# Train the learner on the training ids
learner$train(task, row_ids = ids$train)
print(learner$model)
#> Decision Table:
#>
#> Number of training instances: 139
#> Number of Rules : 30
#> Non matches covered by Majority class.
#> Best first.
#> Start set: no attributes
#> Search direction: forward
#> Stale search after 5 node expansions
#> Total number of subsets evaluated: 611
#> Merit of best subset found: 87.05
#> Evaluation (for feature selection): CV (leave one out)
#> Feature set: 4,35,40,43,49,51,1
# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)
# Score the predictions
predictions$score()
#> classif.ce
#> 0.3188406