Verlag: Springer International Publishing AG, 2023
ISBN 10: 3031387465 ISBN 13: 9783031387463
Sprache: Englisch
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Verlag: Springer, Berlin|Springer International Publishing|Springer, 2023
ISBN 10: 3031387465 ISBN 13: 9783031387463
Sprache: Englisch
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Verlag: Springer International Publishing AG, CH, 2023
ISBN 10: 3031387465 ISBN 13: 9783031387463
Sprache: Englisch
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In den WarenkorbHardback. Zustand: New. 2023 ed. 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, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R(ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.
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Verlag: Springer International Publishing AG, CH, 2023
ISBN 10: 3031387465 ISBN 13: 9783031387463
Sprache: Englisch
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In den WarenkorbHardback. Zustand: New. 2023 ed. 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, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R(ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.
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In den WarenkorbHardcover. Zustand: Brand New. 607 pages. 10.00x7.00x1.50 inches. In Stock.
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Verlag: (Springer, Switzerland, 2023), 2023
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In den WarenkorbFirst Edition. Large Octavo. 607 pages Written by four experts of the field, this book offers an excellent entry to statistical learning to a broad audience, including those without strong background in mathematics. The focus is on explanation of concepts, description of methods, application of software,and interpretation of results. A reader only needs to know the basic concepts from an algebra-based elementary probability course, such as random variables, probability distribution, expectation, variance, conditional probability, and the Bayes theory. A fine copy bound in yellowpurple, red, blue and green paper covered boards lettered in yellow, white, and black.
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