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web.stanford.edu/~hastie
Trevor Hastie is the John A. Overdeck Professor of Statistics at Stanford University. Prior to joining Stanford University, Professor Hastie worked at AT&T Bell Laboratories, where he helped develop the statistical modeling environment popular in the R computing system. Professor Hastie is known for his research in applied statistics, particularly in the fields of data mining, bioinformatics, and
The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. “Big data,” “data science,” and “machine learning” have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on a journey through th
Springer Series in Statistics Trevor Hastie RobertTibshirani Jerome Friedman Springer Series in Statistics The Elements of Statistical Learning Data Mining,Inference,and Prediction TheElementsofStatisticalLearning During the past decade there has been an explosion in computation and information tech- nology. With it have come vast amounts of data in a variety of fields such as medicine, biolo- gy
14-cancer microarray data: Info Training set gene expression , Training set class labels , Test set gene expression , Test set class labels . The indices in the cross-validation folds used in Sec 18.3 are listed in CV folds. Bone Mineral Density: Info Data Larger dataset with ethnicity included: spnbmd.csv Countries: Info Data Galaxy: Info Data Los Angeles Ozone: info Data Marketing: Info Data M
The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Second Edition February 2009 What's new in the 2nd edition? Download the book PDF (corrected 12th printing Jan 2017) Please note: as of 2022 the references in the book to www-stat.stanford.edu/ElemStatLearn should be changed to hastie.su.domains/ElemStatLearn "... a beautiful book". David Hand, Biometrics 2002 "An import
An Introduction to Statistical Learning with Applications in R (second edition) by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani (August 2021) 3 new chapters (+179 pages), including Deep Learning Book Homepage and pdf PAPERS The research reported here was partially supported by grants from the National Science Foundation and the National Institutes of Health. For medical papers
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