Serg Masís

Interpretable Machine Learning with Python (eBook, ePUB)

Build explainable, fair, and robust high-performance models with hands-on, real-world examples

• Format: ePub

Produktbeschreibung

Interpretable Machine Learning with Python, Second Edition, brings to light the key concepts of interpreting machine learning models by analyzing real-world data, providing you with a wide range of skills and tools to decipher the results of even the most complex models.

Build your interpretability toolkit with several use cases, from flight delay prediction to waste classification to COMPAS risk assessment scores. This book is full of useful techniques, introducing them to the right use case. Learn traditional methods, such as feature importance and partial dependence plots to integrated gradients for NLP interpretations and gradient-based attribution methods, such as saliency maps.

In addition to the step-by-step code, you'll get hands-on with tuning models and training data for interpretability by reducing complexity, mitigating bias, placing guardrails, and enhancing reliability.

By the end of the book, you'll be confident in tackling interpretability challenges with black-box models using tabular, language, image, and time series data.

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Produktdetails

• Verlag: Packt Publishing
• Seitenzahl: 606
• Erscheinungstermin: 31. Oktober 2023
• Englisch
• ISBN-13: 9781803243627
• Artikelnr.: 69462658

Autorenporträt

Serg Masi¿s 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.