Interpretable Machine Learning with Python

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?

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 startup, 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 more robustly.