Table of Contents

Interpretation, Interpretability and Explainability; and why does it all matter?

Key Concepts of Interpretability

Interpretation Challenges

Fundamentals of Feature Importance and Impact

Global Model-Agnostic Interpretation Methods

Local Model-Agnostic Interpretation Methods

Anchor and Counterfactual Explanations

Visualizing Convolutional Neural Networks

Interpretation Methods for Multivariate Forecasting and Sensitivity Analysis

Feature Selection and Engineering for Interpretability

Bias Mitigation and Causal Inference Methods

Monotonic Constraints and Model Tuning for Interpretability

Adversarial Robustness

What's Next for Machine Learning Interpretability?

About the Author

Serg Masís has been at the confluence of the internet, application development, and analytics for the last two decades. Currently, he's a climate and agronomic data scientist at Syngenta, a leading agribusiness company with a mission to improve global food security. Before that role, he co-founded a start-up, incubated by Harvard Innovation Labs, that combined the power of cloud computing and machine learning with principles in decision-making science to expose users to new places and events. Whether it pertains to leisure activities, plant diseases, or customer lifetime value, Serg is passionate about providing the often-missing link between data and decision-making—and machine learning interpretation helps bridge this gap robustly.