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Trade-off between accuracy and interpretability for predictive in silico modeling
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
2011 (English)In: Future Medicinal Chemistry, ISSN 1756-8919, E-ISSN 1756-8927, Vol. 3, no 6, p. 647-663Article in journal (Refereed) Published
Abstract [en]

Background: Accuracy concerns the ability of a model to make correct predictions, while interpretability concerns to what degree the model allows for human understanding. Models exhibiting the former property are many times more complex and opaque, while interpretable models may lack the necessary accuracy. The trade-off between accuracy and interpretability for predictive in silico modeling is investigated. Method: A number of state-of-the-art methods for generating accurate models are compared with state-of-the-art methods for generating transparent models. Conclusion: Results on 16 biopharmaceutical classification tasks demonstrate that, although the opaque methods generally obtain higher accuracies than the transparent ones, one often only has to pay a quite limited penalty in terms of predictive performance when choosing an interpretable model.

Place, publisher, year, edition, pages
2011. Vol. 3, no 6, p. 647-663
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:su:diva-68824DOI: 10.4155/FMC.11.23ISI: 000291277500008OAI: oai:DiVA.org:su-68824DiVA, id: diva2:474140
Note

authorCount :4

Available from: 2012-01-09 Created: 2012-01-07 Last updated: 2018-01-12Bibliographically approved

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