5 years ago
Explainable Artificial Intelligence (Part 2) – Model Interpretation Strategies

Originally published in KDnuggets, December 2018

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Introduction

This article is a continuation in my series of articles aimed at ‘Explainable Artificial Intelligence (XAI)’. If you haven’t checked out the first article, I would definitely recommend you to take a quick glance at ‘Part I — The Importance of Human Interpretable Machine Learning’ which covers the what and why of human interpretable machine learning and the need and importance of model interpretation along with its scope and criteria. In this article, we will be picking up from where we left off and expand further into the criteria of machine learning model interpretation methods and explore techniques for interpretation based on scope. The aim of this article is to give you a good understanding of existing, traditional model interpretation methods, their limitations and challenges. We will also cover the classic model accuracy vs. model interpretability trade-off and finally take a look at the major strategies for model interpretation.

Briefly, we will be covering the following aspects in this article.

• Traditional Techniques for Model Interpretation
• Challenges and Limitations of Traditional Techniques
• The Accuracy vs. Interpretability trade-off
• Model Interpretation Techniques

This should get us set and ready for the detailed hands-on guide to model interpretation coming in Part 3, so stay tuned!

Traditional Techniques for Model Interpretation

Model interpretation at heart, is to find out ways to understand model decision making policies better. This is to enable fairness, accountability and transparency which will give humans enough confidence to use these models in real-world problems which a lot of impact to business and society. Hence, there are techniques which have existed for a long time now, which can be used to understand and interpret models in a better way. These can be grouped under the following two major categories.

• Exploratory analysis and visualization techniques like clustering and dimensionality reduction.
• Model performance evaluation metrics like precision, recall, accuracy, ROC curve and the AUC (for classification models) and the coefficient of determination (R-square), root mean-square error, mean absolute error (for regression models)

Let’s briefly take a more detailed look at these techniques.

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About the Author

Data Scientist, Published Author, Mentor & Trainer. Have an interesting proposal, research or opportunity, feel free to message me or email me at dipanzan [dot] sarkar [at] gmail [dot] com