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LogitBoost with simple regression functions as base learners. 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

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.simple_logistic")

Meta Information

  • Task type: “classif”

  • Predict Types: “response”, “prob”

  • Feature Types: “logical”, “integer”, “numeric”, “factor”, “ordered”

  • Required Packages: mlr3, RWeka

Parameters

IdTypeDefaultLevelsRange
subsetuntyped--
na.actionuntyped--
Iinteger-\((-\infty, \infty)\)
SlogicalFALSETRUE, FALSE-
PlogicalFALSETRUE, FALSE-
Minteger-\((-\infty, \infty)\)
Hinteger50\((-\infty, \infty)\)
Wnumeric0\((-\infty, \infty)\)
AlogicalFALSETRUE, FALSE-
output_debug_infologicalFALSETRUE, FALSE-
do_not_check_capabilitieslogicalFALSETRUE, FALSE-
num_decimal_placesinteger2\([1, \infty)\)
batch_sizeinteger100\([1, \infty)\)
optionsuntypedNULL-

References

Landwehr, Niels, Hall, Mark, Frank, Eibe (2005). “Logistic model trees.” Machine learning, 59(1), 161–205.

Sumner M, Frank E, Hall M (2005). “Speeding up Logistic Model Tree Induction.” In 9th European Conference on Principles and Practice of Knowledge Discovery in Databases, 675-683.

See also

Author

damirpolat

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifSimpleLogistic

Methods

Inherited methods


Method new()

Creates a new instance of this R6 class.


Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifSimpleLogistic$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner
learner = mlr3::lrn("classif.simple_logistic")
print(learner)
#> <LearnerClassifSimpleLogistic:classif.simple_logistic>: LogitBoost Based Logistic Regression
#> * Model: -
#> * Parameters: list()
#> * 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)
#> SimpleLogistic:
#> 
#> Class M :
#> -1.86 + 
#> [V1] * 11.54 +
#> [V11] * 3.28 +
#> [V12] * 0.93 +
#> [V16] * -1.31 +
#> [V20] * 0.72 +
#> [V21] * 0.5  +
#> [V27] * 0.49 +
#> [V29] * 0.74 +
#> [V31] * -1.78 +
#> [V36] * -1.65 +
#> [V37] * -0.52 +
#> [V44] * 3.13 +
#> [V45] * 1.38 +
#> [V48] * 2.2  +
#> [V49] * 8.17 +
#> [V50] * -20.08 +
#> [V52] * 40.96 +
#> [V57] * -44.35 +
#> [V58] * 56.39 +
#> [V59] * 46.8 +
#> [V7] * -2.86 +
#> [V8] * -1.69
#> 
#> Class R :
#> 1.86 + 
#> [V1] * -11.54 +
#> [V11] * -3.28 +
#> [V12] * -0.93 +
#> [V16] * 1.31 +
#> [V20] * -0.72 +
#> [V21] * -0.5 +
#> [V27] * -0.49 +
#> [V29] * -0.74 +
#> [V31] * 1.78 +
#> [V36] * 1.65 +
#> [V37] * 0.52 +
#> [V44] * -3.13 +
#> [V45] * -1.38 +
#> [V48] * -2.2 +
#> [V49] * -8.17 +
#> [V50] * 20.08 +
#> [V52] * -40.96 +
#> [V57] * 44.35 +
#> [V58] * -56.39 +
#> [V59] * -46.8 +
#> [V7] * 2.86 +
#> [V8] * 1.69
#> 


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()
#> classif.ce 
#>  0.2028986