An Introduction To Machine Learning Interpretability
DOWNLOAD
Download An Introduction To Machine Learning Interpretability PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get An Introduction To Machine Learning Interpretability book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
An Introduction To Machine Learning Interpretability
DOWNLOAD
Author : Patrick Hall
language : en
Publisher:
Release Date : 2018
An Introduction To Machine Learning Interpretability written by Patrick Hall and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with Artificial intelligence categories.
An Introduction To Machine Learning Interpretability 2nd Edition
DOWNLOAD
Author : Patrick Hall
language : en
Publisher:
Release Date : 2019
An Introduction To Machine Learning Interpretability 2nd Edition written by Patrick Hall and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
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 can also make their predictions difficult to understand. When accuracy outpaces interpretability, human trust suffers, affecting business adoption, model validation efforts, and regulatory oversight. In the updated edition of this ebook, Patrick Hall and Navdeep Gill from H2O.ai 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 a high degree of interpretability. While some industries require model transparency, such as banking, insurance, and healthcare, machine learning practitioners in almost any vertical will likely benefit from incorporating the discussed interpretable models, and debugging, explanation, and fairness approaches into their workflow. This second edition discusses new, exact model explanation techniques, and de-emphasizes the trade-off between accuracy and interpretability. This edition also includes up-to-date information on cutting-edge interpretability techniques and new figures to illustrate the concepts of trust and understanding in machine learning models. Learn how machine learning and predictive modeling are applied in practice Understand social and commercial motivations for machine learning interpretability, fairness, accountability, and transparency Get a definition of interpretability and learn about the groups leading interpretability research Examine a taxonomy for classifying and describing interpretable machine learning approaches Gain familiarity with new and more traditional interpretable modeling approaches See numerous techniques for understanding and explaining models and predictions Read about methods to debug prediction errors, sociological bias, and security vulnerabilities in predictive models Get a feel for the techniques in action with code examples.
An Introduction To Machine Learning Interpretability Dataiku Version
DOWNLOAD
Author : Patrick Hall
language : en
Publisher:
Release Date : 2018
An Introduction To Machine Learning Interpretability Dataiku Version written by Patrick Hall and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.
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.
An Introduction To Machine Learning Interpretability
DOWNLOAD
Author : Patrick Hall
language : en
Publisher:
Release Date : 2018
An Introduction To Machine Learning Interpretability written by Patrick Hall and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with Artificial intelligence categories.
Introduction To Machine Learning Interpretability 2nd Edition
DOWNLOAD
Author : Navdeep Gill
language : en
Publisher:
Release Date : 2019
Introduction To Machine Learning Interpretability 2nd Edition written by Navdeep Gill and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
Explainable Artificial Intelligence An Introduction To Interpretable Machine Learning
DOWNLOAD
Author : Uday Kamath
language : en
Publisher: Springer Nature
Release Date : 2021-12-15
Explainable Artificial Intelligence An Introduction To Interpretable Machine Learning written by Uday Kamath and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-12-15 with Computers categories.
This book is written both for readers entering the field, and for practitioners with a background in AI and an interest in developing real-world applications. The book is a great resource for practitioners and researchers in both industry and academia, and the discussed case studies and associated material can serve as inspiration for a variety of projects and hands-on assignments in a classroom setting. I will certainly keep this book as a personal resource for the courses I teach, and strongly recommend it to my students. --Dr. Carlotta Domeniconi, Associate Professor, Computer Science Department, GMU This book offers a curriculum for introducing interpretability to machine learning at every stage. The authors provide compelling examples that a core teaching practice like leading interpretive discussions can be taught and learned by teachers and sustained effort. And what better way to strengthen the quality of AI and Machine learning outcomes. I hope that this book will become a primer for teachers, data Science educators, and ML developers, and together we practice the art of interpretive machine learning. --Anusha Dandapani, Chief Data and Analytics Officer, UNICC and Adjunct Faculty, NYU This is a wonderful book! I’m pleased that the next generation of scientists will finally be able to learn this important topic. This is the first book I’ve seen that has up-to-date and well-rounded coverage. Thank you to the authors! --Dr. Cynthia Rudin, Professor of Computer Science, Electrical and Computer Engineering, Statistical Science, and Biostatistics & Bioinformatics Literature on Explainable AI has up until now been relatively scarce and featured mainly mainstream algorithms like SHAP and LIME. This book has closed this gap by providing an extremely broad review of various algorithms proposed in the scientific circles over the previous 5-10 years. This book is a great guide to anyone who is new to the field of XAI or is already familiar with the field and is willing to expand their knowledge. A comprehensive review of the state-of-the-art Explainable AI methods starting from visualization, interpretable methods, local and global explanations, time series methods, and finishing with deep learning provides an unparalleled source of information currently unavailable anywhere else. Additionally, notebooks with vivid examples are a great supplement that makes the book even more attractive for practitioners of any level. Overall, the authors provide readers with an enormous breadth of coverage without losing sight of practical aspects, which makes this book truly unique and a great addition to the library of any data scientist. Dr. Andrey Sharapov, Product Data Scientist, Explainable AI Expert and Speaker, Founder of Explainable AI-XAI Group
Interpretable Machine Learning
DOWNLOAD
Author : Christoph Molnar
language : en
Publisher: Lulu.com
Release Date : 2020
Interpretable Machine Learning written by Christoph Molnar and has been published by Lulu.com this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Computers categories.
This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
Introduction To Machine Learning
DOWNLOAD
Author : Ethem Alpaydin
language : en
Publisher: MIT Press (MA)
Release Date : 2010
Introduction To Machine Learning written by Ethem Alpaydin and has been published by MIT Press (MA) this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with Computers categories.
A new edition of an introductory text in machine learning that gives a unified treatment of machine learning problems and solutions.
Interpretable Ai
DOWNLOAD
Author : Ajay Thampi
language : en
Publisher: Simon and Schuster
Release Date : 2022-07-05
Interpretable Ai written by Ajay Thampi and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-07-05 with Computers categories.
AI doesn't have to be a black box. These practical techniques help shine a light on your model's mysterious inner workings. Make your AI more transparent, and you'll improve trust in your results, combat data leakage and bias, and ensure compliance with legal requirements. Interpretable AI opens up the black box of your AI models. It teaches cutting-edge techniques and best practices that can make even complex AI systems interpretable. Each method is easy to implement with just Python and open source libraries. You'll learn to identify when you can utilize models that are inherently transparent, and how to mitigate opacity when your problem demands the power of a hard-to-interpret deep learning model.
Machine Learning Interpretability Explaining Ai Models To Humans
DOWNLOAD
Author : Dr. Faisal Alghayadh
language : en
Publisher: Xoffencerpublication
Release Date : 2024-01-10
Machine Learning Interpretability Explaining Ai Models To Humans written by Dr. Faisal Alghayadh and has been published by Xoffencerpublication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-01-10 with Computers categories.
Within the ever-evolving realm of artificial intelligence (AI), the field of Machine Learning Interpretability (MLI) has surfaced as a crucial conduit, serving as a vital link between the intricate nature of sophisticated AI models and the pressing necessity for lucid decision-making procedures in practical scenarios. With the progressive integration of AI systems across various domains, ranging from healthcare to finance, there arises an escalating need for transparency and accountability concerning the operational mechanisms of these intricate models. The pursuit of interpretability in machine learning is of paramount importance in comprehending the enigmatic essence of artificial intelligence. It provides a structured methodology to unravel the intricate mechanisms of algorithms, thereby rendering their outputs intelligible to human stakeholders. The Multimodal Linguistic Interface (MLI) functions as a pivotal conduit, bridging the dichotomous domains of binary machine intelligence and the intricate cognitive faculties of human comprehension. Its primary purpose lies in fostering a mutually beneficial association, wherein the potential of artificial intelligence can be harnessed with efficacy and conscientiousness. The transition from perceiving AI as a "black box" to embracing a more transparent and interpretable framework represents a significant paradigm shift. This shift not only fosters trust in AI technologies but also empowers various stakeholders such as end-users, domain experts, and policymakers. By gaining a deeper understanding of AI model outputs, these stakeholders are equipped to make informed decisions with confidence. In the current epoch characterized by remarkable progress in technology, the importance of Machine Learning Interpretability is underscored as a pivotal element for the conscientious and ethical implementation of AI. This development heralds a novel era wherein artificial intelligence harmoniously interfaces with human intuition and expertise