Classification Voted Perceptron Learner
mlr_learners_classif.voted_perceptron.Rd
Voted Perceptron Algorithm by Freund and Schapire.
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
Parameters
Id | Type | Default | Levels | Range |
subset | untyped | - | - | |
na.action | untyped | - | - | |
I | integer | 1 | \([1, \infty)\) | |
E | numeric | 1 | \((-\infty, \infty)\) | |
S | integer | 1 | \((-\infty, \infty)\) | |
M | integer | 10000 | \((-\infty, \infty)\) | |
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 | - |
References
Freund Y, Schapire RE (1998). “Large margin classification using the perceptron algorithm.” In 11th Annual Conference on Computational Learning Theory, 209-217.
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
-> LearnerClassifVotedPerceptron
Examples
# Define the Learner
learner = mlr3::lrn("classif.voted_perceptron")
print(learner)
#> <LearnerClassifVotedPerceptron:classif.voted_perceptron>: Voted Perceptron
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, RWeka
#> * Predict Types: [response], prob
#> * Feature Types: logical, integer, numeric, factor, ordered
#> * Properties: missings, 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$model)
#> VotedPerceptron: Number of perceptrons=62
# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)
# Score the predictions
predictions$score()
#> classif.ce
#> 0.3043478