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.
Keywords
InterpretabilityComputer scienceMachine learningArtificial intelligenceDecision treeBayesian networkBayesian probabilityData mining
Affiliated Institutions
Related Publications
Introduction to Data Mining
1 Introduction 1.1 What is Data Mining? 1.2 Motivating Challenges 1.3 The Origins of Data Mining 1.4 Data Mining Tasks 1.5 Scope and Organization of the Book 1.6 Bibliog...
A Simple Relational Classifier
We analyze a Relational Neighbor (RN) classifier, a simple relational\npredictive model that predicts only based on class labels of related neighbors,\nusing no learning and no ...
Additive logistic regression: a statistical view of boosting (With discussion and a rejoinder by the authors)
Boosting is one of the most important recent developments in\nclassification methodology. Boosting works by sequentially applying a\nclassification algorithm to reweighted versi...
A Communication-Efficient Parallel Algorithm for Decision Tree
Decision tree (and its extensions such as Gradient Boosting Decision Trees and Random Forest) is a widely used machine learning algorithm, due to its practical effectiveness and...
On a Method to Measure Supervised Multiclass Model’s Interpretability: Application to Degradation Diagnosis (Short Paper)
In an industrial maintenance context, degradation diagnosis is the problem of determining the current level of degradation of operating machines based on measurements. With the ...
Publication Info
- Year
- 2014
- Type
- article
- Volume
- 15
- Issue
- 1
- Pages
- 1-10
- Citations
- 548
- Access
- Closed
External Links
Social Impact
Altmetric
PlumX Metrics
Social media, news, blog, policy document mentions
Citation Metrics
548
OpenAlex
Cite This
Alex A. Freitas
(2014).
Comprehensible classification models.
ACM SIGKDD Explorations Newsletter
, 15
(1)
, 1-10.
https://doi.org/10.1145/2594473.2594475
Identifiers
- DOI
- 10.1145/2594473.2594475