The DOI matches its Crossref record; 64 of the 64 references it cites were found in a registry or an index.
Comprehensible classification models: a position paper
Abstract
The vast majority of the literature evaluates the performance of classification models using only the criterion of predictive accuracy. This paper reviews the case for considering also the comprehensibility (interpretability) of classification models, and discusses the interpretability of five types of classification models, namely decision trees, classification rules, decision tables, nearest neighbors and Bayesian network classifiers. We discuss both interpretability issues which are specific to each of those model types and more generic interpretability issues, namely the drawbacks of using model size as the only criterion to evaluate the comprehensibility of a model, and the use of monotonicity constraints to improve the comprehensibility and acceptance of classification models by users.
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Cited by
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939778 · Cited by 17,555
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Metadata
- Authors
- Freitas, A. A.
- Venue
- ACM SIGKDD Explorations Newsletter
- Published
- 2014-03-17
- Volume
- 15
- Issue
- 1
- Pages
- 1-10
- ISSN
- 1931-0145 (ISSN-L)
- 1931-0153
- OpenAlex
- W2026905436
- Cited by
- 572
- References
- 64
- FWCI
- 25.09
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
