Interpretable Boosted Naïve Bayes Classification

By Greg Ridgeway, David Madigan, Thomas S. Richardson, J. O'Kane. 1998

How researchers see this paper

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Citations in the corpus

The mythos of model interpretability (2016, background): “Many papers motivate interpretability as a means to engender trust (Kim, 2015; Ridgeway et al., 1998).”
Interpretable Decision Sets: A Joint Framework for Description and Prediction (2016, background): “Research on interpretable models has a long history [8, 32, 7, 33], but with the availability of ever larger datasets and the adoption of computer-based decision making in all parts of society the need for interpretable machine learning models has only increased.”
Top 10 algorithms in data mining (2007, unknown): “Some such modifications are described in [27,66].”
Seeing the Forest Through the Trees (2007, background): “Some authors have pointed out that striving for comprehensibility is one of the important issues in ensemble learning requiring future investigations [1,16].”
Asking 'Why' in AI: Explainability of intelligent systems - perspectives and challenges (2018, background): “Moreover, as in the classical AI consideration of explanations, interpretations in ML were seen as critical in building user trust in the system (Ridgeway, Madigan, and Richardson, 1998).”
Spectral Algorithms for Computing Fair Support Vector Machines (2017, background): “The first is a general lack of interpretability for how predictions are produced by many learning techniques [Ridgeway et al., 1998, Lou et al., 2012, Caruana et al., 2015], and the second is the possibility of perpetuating inequities that may be present in the training data [Podesta et al., 2014,…”
Interacting meaningfully with machine learning systems: Three experiments (2009, methodology): “…regression and linear support vector machines, can be explained through showing how the weight on each feature contributes to the overall prediction and a graph or chart can be used for visualization (Becker et al. 2001; Kononenko 1993; Mozina et al. 2004; Poulin et al. 2006, Ridgeway et al. 1998).”