Interpretable Machine Learning with Python

By Serg Masís
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About this book

Machine learning (ML) interpretation enables practitioners to understand their models and mitigate risks associated with poor predictions.

The first section is a beginner's guide to interpretability, and it starts by recognizing its relevance in business and exploring its key aspects and challenges. It will reveal the inner workings of white-box models and contrast them to black-box and glass-box models and examine their trade-off. The second section is about mastering a vast array of interpretation methods while applying them to different use-cases. Model-agnostic methods studied range from permutation importance to SHAP and counterfactuals to sensitivity analysis. In addition to the step-by-step code, there's a strong focus on interpreting model outcomes in the context of each chapter's example. The third section is about tuning models and training data for interpretability by reducing complexity, mitigating bias, placing guardrails, and enhancing reliability. The methods explored here range from state-of-the-art feature selection and dataset debiasing methods to monotonic constraints and adversarial retraining.

By the end of this book, you will understand ML models better and enhance them through interpretability tuning.

Publication date:
February 2021

About the Author

  • Serg Masís

    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 search engine 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 efficiently. Whether it pertains to leisure activities, plant diseases, or poker hands, Serg is passionate about providing the often-missing link between data and decision-making.

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