NSR content is relevant for a wide range of professinals, including:An Introduction to Statistical Learning: With Applications in R Springer Texts in Statistics, Book 103 By: Gareth James, Daniela Witten, Trevor Hastie, and others Narrated by: Zachary Klein Length: 11 hrs and 15 minsIntroduction to Statistical Learning. Flashcards I made while working my way through the textbook Introduction to Statistical Learning by Hastie, Tibshirani et al.
ECONOMICS Macroenomics Book Chapter 1.Details: An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along. As a clear and concise alternative to a textbook, this book provides a practical and high-level introduction to the practical components and statistical.You may begin your exploration of NSR by browsing the three main channels— Highlights, Reports, and the Resource Center—or by selecting one of the categories.The Highlights section features the latest news and developments pertaining to a wide variety of topics related to the evolution of digital content as related to research, publishing, education and libraries. These include various press releases announcing ground-breaking initiatives, webinar and podcast alerts, conference summaries, research and survey findings, and more.The Reports section features lengthy research articles originally published on No Shelf Required (authored by Dr. Mirela Roncevic and contributors), all exploring issues relevant to open science, open access publishing, copyright, Digital Rights Management (DRM), Open Educational Resouces (OERs), open peer review, and more.The Resouce Center is a (constantly growing) repository and directory of resources online for further inquiry. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers.
3.3.2 Global, Holistic Model Interpretability 3.2 Taxonomy of Interpretability Methods Here you will find useful links to recommended papers, online courses, case studies, and platforms for research where scholarly content may be accessed freely and without restriction. The Resource Center is NSR’s learning corner.
4.3 Risk Factors for Cervical Cancer (Classification) 4.2 YouTube Spam Comments (Text Classification) 3.3.5 Local Interpretability for a Group of Predictions 3.3.4 Local Interpretability for a Single Prediction
10.5.4 Disadvantages of Identifying Influential InstancesMachine learning has great potential for improving products, processes and research.But computers usually do not explain their predictions which is a barrier to the adoption of machine learning.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.The focus of the book is on model-agnostic methods for interpreting black box models such as feature importance and accumulated local effects, and explaining individual predictions with Shapley values and LIME.In addition, the book presents methods specific to deep neural networks.All interpretation methods are explained in depth and discussed critically.This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.Reading the book is recommended for machine learning practitioners, data scientists, statisticians, and anyone else interested in making machine learning models interpretable.You can buy the PDF and e-book version (epub, mobi) on leanpub.com.You can buy the print version on lulu.com.About me: My name is Christoph Molnar, I’m a statistician and a machine learner.My goal is to make machine learning interpretable.Follow me on Twitter! by book is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. 10.5.3 Advantages of Identifying Influential Instances 10.3.5 Bonus: Other Concept-based Approaches 10.3.1 TCAV: Testing with Concept Activation Vectors
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