An introduction to statistical learning used

#AN INTRODUCTION TO STATISTICAL LEARNING USED SOFTWARE#

Even if you donвЂ™t want to become a data analyst-which happens to be one of the fastest-growing jobs out there, just so you know-these books are invaluable guides to help explain whatвЂ™s going on.вЂќ (Pocket, February 23, 2018) вЂњData and statistics are an increasingly important part of modern life, and nearly everyone would be better off with a deeper understanding of the tools that help explain our world. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap.

#AN INTRODUCTION TO STATISTICAL LEARNING USED SOFTWARE#

Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Trevor Hastie and Robert Tibshirani are professors of statistics at Stanford University, and are co-authors of the successful textbook Elements of Statistical Learning. Her research focuses largely on statistical machine learning in the high-dimensional setting, with an emphasis on unsupervised learning. The conceptual framework for this book grew out of his MBA elective courses in this area.Дaniela WittenВ is an associate professor of statistics and biostatistics at the University of Washington.

He has published an extensive body of methodological work in the domain of statistical learning with particular emphasis on high-dimensional and functional data. Gareth JamesВ is a professor of data sciences and operations at the University of Southern California. The text assumes only a previous course in linear regression and no knowledge of matrix algebra. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. 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. 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. 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.

An introduction to statistical learning used