Beyond predictive accuracy : what?

By Christophe G. Giraud-Carrier. 1998

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Citations in the corpus

Learning Certifiably Optimal Rule Lists (2017, background): “comprehensible) models because of their societal importance (see [3, 11, 16, 18, 20, 21, 38, 39, 42]).”
Scalable Bayesian Rule Lists (2016, methodology): “Interpretability has long since been a fundamental topic in artiﬁcial intelligence (see R¨uping, 2006; Bratko, 1997; Dawes, 1979; Vellido, Mart´ın-Guerrero, & Lisboa, 2012; Giraud-Carrier, 1998; Holte, 1993; Shmueli, 2010; Huysmans, Dejaeger, Mues, Vanthienen, & Baesens, 2011; Freitas, 2014).”
Learning interpretable models (2006, background): “A main problem is the fact that there are several other performance criteria for a knowledge discovery system than predictive accuracy [Giraud-Carrier, 1998], which is usually the main point of concern when evaluating a machine learning algorithm.”
A Perspective View and Survey of Meta-Learning (2002, background): “Gama and Brazdil (1995) extract domain characteristics such as the number of examples, number of attributes, number of classes, standard deviation ratio for each feature, skewness, kurtosis, noise-signal ratio, etc., to generate metarule models.”
Building Interpretable Classifiers with Rules using Bayesian Analysis (2012, background): “In many domains, interpretability is a fundamental desirable quality in a predictive model [17] [41].”
An interpretable model for stroke prediction using rules and Bayesian analysis (2014, background): “In many domains, interpretability is a fundamental desirable quality in a predictive model [5].”
Interpretability of machine learning models and representations: an introduction (2016, background): “Other authors use the term interpretability as strict synonym of understandability [5,10] or comprehensibility [3,6,8,13].”
Comprehensibility of Classification Trees–Survey Design (2019, methodology): “A metric of comprehensibility is therefore needed in order to compare learning systems performance and as a (part of) heuristic function used by a learning algorithm [9, 21].”
Using Meta-Learning to Support Data Mining (2004, methodology): “Criteria such as accuracy, storage space, and running time can be used for performance assessment ([27]; Fig.”