Comprehensible classification models - a position paper

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.