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An introduction to machine learning interpretability : an applied perspective on fairness, accountability, transparency, and explainable AI /
Innovation and competition are driving analysts and data scientists toward increasingly complex predictive modeling and machine learning algorithms. This complexity makes these models accurate but also makes their predictions difficult to understand. When accuracy outpaces interpretability, human tr...
| Call Number: | Libro Electrónico |
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| Main Authors: | , |
| Format: | Electronic eBook |
| Language: | Inglés |
| Published: |
Sebastopol, CA :
O'Reilly Media,
[2018]
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| Edition: | First edition. |
| Subjects: | |
| Online Access: | Texto completo (Requiere registro previo con correo institucional) |
| Summary: | Innovation and competition are driving analysts and data scientists toward increasingly complex predictive modeling and machine learning algorithms. This complexity makes these models accurate but also makes their predictions difficult to understand. When accuracy outpaces interpretability, human trust suffers, affecting business adoption, regulatory oversight, and model documentation. Banking, insurance, and healthcare in particular require predictive models that are interpretable. In this ebook, Patrick Hall and Navdeep Gill from H2O.ai thoroughly introduce the idea of machine learning interpretability and examine a set of machine learning techniques, algorithms, and models to help data scientists improve the accuracy of their predictive models while maintaining interpretability. Learn how machine learning and predictive modeling are applied in practice Understand social and commercial motivations for machine learning interpretability, fairness, accountability, and transparency Explore the differences between linear models and more accurate machine learning models Get a definition of interpretability and learn about the groups leading interpretability research Examine a taxonomy for classifying and describing interpretable machine learning approaches Learn several practical techniques for data visualization, training interpretable machine learning models, and generating explanations for complex model predictions Explore automated approaches for testing model interpretability. |
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| Physical Description: | 1 online resource (1 volume) : illustrations |
| Bibliography: | Includes bibliographical references. |
| ISBN: | 1492033146 9781492033141 |