mlr_measures_iml_main_effect_complexity: IML Main Effect Complexity

In sumny/iaml_prototype: Interpretable (auto) ML within the mlr3 ecosystem via multi-objective optimization

IML Main Effect Complexity

Description

Measures the main effect complexity of features of a model according to Molnar et al. (2020). Note that the models must be stored to be able to extract this information.

This measure requires the mlr3::Task and the mlr3::Learner for scoring.

Dictionary

This Measure can be instantiated via the dictionary mlr_measures or with the associated sugar function msr():

mlr_measures$get("iml_main_effect_complexity")
msr("iml_main_effect_complexity")

Meta Information

• Task type: “NA”

• Range: [0, \infty)

• Minimize: TRUE

• Average: macro

• Required Prediction: “prob”

• Required Packages: mlr3

Parameters

Id	Type	Default	Levels
normalize	logical	FALSE	TRUE, FALSE

Super class

mlr3::Measure -> MeasureIMLMEC

Methods

Public methods

• MeasureIMLMEC$new()

• MeasureIMLMEC$clone()

Inherited methods

• mlr3::Measure$aggregate()

• mlr3::Measure$format()

• mlr3::Measure$help()

• mlr3::Measure$print()

• mlr3::Measure$score()

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Method new()

Creates a new instance of this R6 class.

Usage

MeasureIMLMEC$new()

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Method clone()

The objects of this class are cloneable with this method.

Usage

MeasureIMLMEC$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

References

Molnar, Christoph, Casalicchio, Giuseppe, Bischl, Bernd (2020). “Quantifying Model Complexity via Functional Decomposition for Better Post-Hoc Interpretability.” In Machine Learning and Knowledge Discovery in Databases, 193–204.

sumny/iaml_prototype documentation built on May 16, 2023, 8:27 p.m.