Gespeichert in:
Beteiligte Personen: | , |
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Format: | Elektronisch E-Book |
Sprache: | Englisch |
Veröffentlicht: |
Sebastopol, CA
O'Reilly Media
[2018]
|
Ausgabe: | First edition. |
Schlagwörter: | |
Links: | https://learning.oreilly.com/library/view/-/9781492033158/?ar |
Zusammenfassung: | 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. |
Beschreibung: | Includes bibliographical references. - Online resource; title from title page (Safari, viewed April 17, 2018) |
Umfang: | 1 Online-Ressource (1 volume) illustrations |
ISBN: | 1492033146 9781492033141 |
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spelling | Hall, Patrick VerfasserIn aut An introduction to machine learning interpretability an applied perspective on fairness, accountability, transparency, and explainable AI Patrick Hall and Navdeep Gill Applied perspective on fairness, accountability, transparency, and explainable AI First edition. Sebastopol, CA O'Reilly Media [2018] ©2018 1 Online-Ressource (1 volume) illustrations Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Includes bibliographical references. - Online resource; title from title page (Safari, viewed April 17, 2018) 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. Machine learning Artificial intelligence Apprentissage automatique Intelligence artificielle artificial intelligence Gill, Navdeep VerfasserIn aut |
spellingShingle | Hall, Patrick Gill, Navdeep An introduction to machine learning interpretability an applied perspective on fairness, accountability, transparency, and explainable AI Machine learning Artificial intelligence Apprentissage automatique Intelligence artificielle artificial intelligence |
title | An introduction to machine learning interpretability an applied perspective on fairness, accountability, transparency, and explainable AI |
title_alt | Applied perspective on fairness, accountability, transparency, and explainable AI |
title_auth | An introduction to machine learning interpretability an applied perspective on fairness, accountability, transparency, and explainable AI |
title_exact_search | An introduction to machine learning interpretability an applied perspective on fairness, accountability, transparency, and explainable AI |
title_full | An introduction to machine learning interpretability an applied perspective on fairness, accountability, transparency, and explainable AI Patrick Hall and Navdeep Gill |
title_fullStr | An introduction to machine learning interpretability an applied perspective on fairness, accountability, transparency, and explainable AI Patrick Hall and Navdeep Gill |
title_full_unstemmed | An introduction to machine learning interpretability an applied perspective on fairness, accountability, transparency, and explainable AI Patrick Hall and Navdeep Gill |
title_short | An introduction to machine learning interpretability |
title_sort | an introduction to machine learning interpretability an applied perspective on fairness accountability transparency and explainable ai |
title_sub | an applied perspective on fairness, accountability, transparency, and explainable AI |
topic | Machine learning Artificial intelligence Apprentissage automatique Intelligence artificielle artificial intelligence |
topic_facet | Machine learning Artificial intelligence Apprentissage automatique Intelligence artificielle artificial intelligence |
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