A Categorisation of Post-hoc Explanations for Predictive Models

Abstract

The ubiquity of machine learning based predictive models in modern society naturally leads people to ask how trustworthy those models are? In predictive modeling, it is quite common to induce a trade-off between accuracy and interpretability. For instance, doctors would like to know how effective some treatment will be for a patient or why the model suggested a particular medication for a patient exhibiting those symptoms? We acknowledge that the necessity for interpretability is a consequence of an incomplete formalisation of the problem, or more precisely of multiple meanings adhered to a particular concept. For certain problems, it is not enough to get the answer (what), the model also has to provide an explanation of how it came to that conclusion (why), because a correct prediction, only partially solves the original problem. In this article we extend existing categorisation of techniques to aid model interpretability and test this categorisation.

5 pages, 3 figures, AAAI 2019 Spring Symposia (#SSS19)

References in corpus (9)

• Explaining NonLinear Classification Decisions with Deep Taylor Decomposition
• Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model
• How to Explain Individual Classification Decisions
• Consistent Individualized Feature Attribution for Tree Ensembles
• Visualizing Deep Convolutional Neural Networks Using Natural Pre-Images
• Interpreting Classifiers through Attribute Interactions in Datasets
• Interpretable Low-Dimensional Regression via Data-Adaptive Smoothing
• Bayesian Patchworks: An Approach to Case-Based Reasoning
• Meaningful Models: Utilizing Conceptual Structure to Improve Machine Learning Interpretability