An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. 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.
An introduction to statistical learning review software#
Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Color graphics and real-world examples are used to illustrate the methods presented. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. This book presents some of the most important modeling and prediction techniques, along with relevant applications.
7th Printing 2017 Edition (29 September 2017)Īn 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. There is also a chapter on methods for wide data (p bigger than n), including multiple testing and false discovery rates. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorisation, and spectral clustering. The many topics include neural networks, support vector machines, classification trees and boosting-the first comprehensive treatment of this topic in any book. The book coverage is broad, from supervised learning (prediction) to unsupervised learning.
It is a valuable resource for statisticians and anyone interested in data mining in science or industry. Many examples are given, with a liberal use of colour graphics. While the approach is statistical, the emphasis is on concepts rather than mathematics. Incorporation of writing discussion sections into each of the three methodologies.Ĭurrent references and additional readings are included in this new edition.ĭescription This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework.
Incorporation of action research and program evaluation in mixed methods and coverage of the latest advances in the mixed methods fieldĪdditional coverage on qualitative and quantitative data analysis software in the respective methods chapters.Īdditional information about causality and its relationship to statistics in quantitative methods. New to this Edition Updated discussion on designing a proposal for a research project and on the steps in designing a research study.Īdditional content on epistemological and ontological positioning in relation to the research question and chosen methodology and method.Īdditional updates on the transformative worldview.Įxpanded coverage on specific approaches such as case studies, participatory action research, and visual methods.Īdditional information about social media, online qualitative methods, and mentoring and reflexivity in qualitative methods. David Creswell include a preliminary consideration of philosophical assumptions key elements of the research process a review of the literature an assessment of the use of theory in research applications, and reflections about the importance of writing and ethics in scholarly inquiry. This bestselling text pioneered the comparison of qualitative, quantitative, and mixed methods research design. Sage Publications,Inc Fifth Edition (January 2,2018)
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