Explainable AI for Practitioners : Designing and Implementing Explainable ML Solutions

Michael Munn

ISBN 10: 1098119134 / ISBN 13: 9781098119133

Editore: O'reilly Media Dez 2022, 2022

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Neuware - Most intermediate-level machine learning books focus on how to optimize models by increasing accuracy or decreasing prediction error. But this approach often overlooks the importance of understanding why and how your ML model makes the predictions that it does.

Codice articolo 9781098119133

Dati bibliografici

• Titolo: Explainable AI for Practitioners : Designing and Implementing Explainable ML Solutions
• Editore: O'reilly Media Dez 2022
• Data di pubblicazione: 2022
• Lingua: Inglese
• ISBN 10: 1098119134
• ISBN 13: 9781098119133
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 232 L x 178 A x 17 P (millimetri) 232 millimetri di larghezza per 178 millimetri di altezza per 17 millimetri di profondità
• Peso: 484 grammi

Descrizione articolo

Riassunto

Most intermediate-level machine learning books focus on how to optimize models by increasing accuracy or decreasing prediction error. But this approach often overlooks the importance of understanding why and how your ML model makes the predictions that it does.

Explainability methods provide an essential toolkit for better understanding model behavior, and this practical guide brings together best-in-class techniques for model explainability. Experienced machine learning engineers and data scientists will learn hands-on how these techniques work so that you'll be able to apply these tools more easily in your daily workflow.

This essential book provides:

• A detailed look at some of the most useful and commonly used explainability techniques, highlighting pros and cons to help you choose the best tool for your needs
• Tips and best practices for implementing these techniques
• A guide to interacting with explainability and how to avoid common pitfalls
• The knowledge you need to incorporate explainability in your ML workflow to help build more robust ML systems
• Advice about explainable AI techniques, including how to apply techniques to models that consume tabular, image, or text data
• Example implementation code in Python using well-known explainability libraries for models built in Keras and TensorFlow 2.0, PyTorch, and HuggingFace