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Interpretable Machine Learning with Python


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Table of Contents

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.

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