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Interpretable Machine Learning with Python
Interpretable Machine Learning with Python

Interpretable Machine Learning with Python

Serg , Masís

Paperback / Ingenaaid | Engels
  • Leverbaar, de levertijd is 6 werkdagen.
  • Niet op voorraad in onze winkel
€ 70,70
Omschrijving

Understand the key aspects and challenges of machine learning interpretability, learn how to overcome them with interpretation methods, and leverage them to build fairer, safer, and more reliable modelsKey Features:Learn how to extract easy-to-understand insights from any machine learning modelBecome well-versed with interpretability techniques to build fairer, safer, and more reliable modelsMitigate risks in AI systems before they have broader implications by learning how to debug black-box modelsBook Description:Do you want to understand your models and mitigate risks associated with poor predictions using machine learning (ML) interpretation? Interpretable Machine Learning with Python can help you work effectively with ML models.The first section of the book is a beginner's guide to interpretability, covering its relevance in business and exploring its key aspects and challenges. You'll focus on how white-box models work, compare them to black-box and glass-box models, and examine their trade-off. The second section will get you up to speed with a vast array of interpretation methods, also known as Explainable AI (XAI) methods, and how to apply them to different use cases, be it for classification or regression, for tabular, time-series, image or text. In addition to the step-by-step code, the book also helps the reader to interpret model outcomes using examples. In the third section, you'll get hands-on with tuning models and training data for interpretability by reducing complexity, mitigating bias, placing guardrails, and enhancing reliability. The methods you'll explore 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'll be able to understand ML models better and enhance them through interpretability tuning.What You Will Learn: Recognize the importance of interpretability in businessStudy models that are intrinsically interpretable such as linear models, decision trees, and Naïve BayesBecome well-versed in interpreting models with model-agnostic methodsVisualize how an image classifier works and what it learnsUnderstand how to mitigate the influence of bias in datasetsDiscover how to make models more reliable with adversarial robustnessUse monotonic constraints to make fairer and safer modelsWho this book is for:This book is for data scientists, machine learning developers, and data stewards who have an increasingly critical responsibility to explain how the AI systems they develop work, their impact on decision making, and how they identify and manage bias. Working knowledge of machine learning and the Python programming language is expected.

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.

Specificaties
  • Uitgever
    Packt Publishing
  • Verschenen
    mrt. 2021
  • Bladzijden
    736
  • Genre
    Expertsystemen / kennissystemen
  • Afmetingen
    235 x 191 x 39 mm
  • Gewicht
    1349 gram
  • EAN
    9781800203907
  • Paperback / Ingenaaid
    Paperback / Ingenaaid
  • Taal
    Engels

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