On the Art and Science of Machine Learning Explanations

By Patrick Hall. 2018

34 citations recorded by the source corpus.

This text discusses several explanatory methods that go beyond the error measurements and plots traditionally used to assess machine learning models. Some of the methods are tools of the trade while others are rigorously derived and backed by long-standing theory. The methods, decision tree surrogate models, individual conditional expectation (ICE) plots, local interpretable model-agnostic explanations (LIME), partial dependence plots, and Shapley explanations, vary in terms of scope, fidelity, and suitable application domain. Along with descriptions of these methods, this text presents real-world usage recommendations supported by a use case and in-depth software examples.

Subfields: Artificial Intelligence, Information Systems and Management, Safety, Robustness & Interpretability

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In Artificial Intelligence

In Information Systems and Management

Francisco Herrera

In Safety, Robustness & Interpretability

Citations in the corpus

Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI (2019, positive): “All these means that the interpretation of the system should, in order to be considered practical, provide either an understanding of the model mechanisms and predictions, a visualization of the model’s discrimination rules, or hints on what could perturb the model [11] .”
A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer’s disease (2021, background): “Because AD is a complex disease and RF is a complex model, in order to have a global comprehensive, consistent, and accurate picture about AD progression, several explanatory techniques are required 88 .”