Package: iml 0.11.3

Giuseppe Casalicchio

iml: Interpretable Machine Learning

Interpretability methods to analyze the behavior and predictions of any machine learning model. Implemented methods are: Feature importance described by Fisher et al. (2018) <doi:10.48550/arxiv.1801.01489>, accumulated local effects plots described by Apley (2018) <doi:10.48550/arxiv.1612.08468>, partial dependence plots described by Friedman (2001) <www.jstor.org/stable/2699986>, individual conditional expectation ('ice') plots described by Goldstein et al. (2013) <doi:10.1080/10618600.2014.907095>, local models (variant of 'lime') described by Ribeiro et. al (2016) <doi:10.48550/arXiv.1602.04938>, the Shapley Value described by Strumbelj et. al (2014) <doi:10.1007/s10115-013-0679-x>, feature interactions described by Friedman et. al <doi:10.1214/07-AOAS148> and tree surrogate models.

Authors:Giuseppe Casalicchio [aut, cre], Christoph Molnar [aut], Patrick Schratz [aut]

iml_0.11.3.tar.gz
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iml.pdf |iml.html✨
iml/json (API)
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Bug tracker:https://github.com/giuseppec/iml/issues

On CRAN:

10 exports 491 stars 7.58 score 40 dependencies 4 dependents 14 mentions 410 scripts 4.1k downloads

Last updated 5 months agofrom:9c08213af0. Checks:OK: 7. Indexed: yes.

Exports:FeatureEffectFeatureEffectsFeatureImpInteractionInterpretationMethodLocalModelPartialPredictorShapleyTreeSurrogate

Dependencies:backportscheckmateclicodetoolscolorspacedata.tabledigestfansifarverFormulafuturefuture.applyggplot2globalsgluegtableisobandlabelinglatticelifecyclelistenvmagrittrMASSMatrixMetricsmgcvmunsellnlmeparallellypillarpkgconfigR6RColorBrewerrlangscalestibbleutf8vctrsviridisLitewithr