# ↠´ Read Ã The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics) eBook: Trevor Hastie, Robert Tibshirani, Jerome Friedman: Amazon.ca: Kindle Store by Trevor Hastie Ó izmirescort.pro

↠´ Read Ã The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics) eBook: Trevor Hastie, Robert Tibshirani, Jerome Friedman: Amazon.ca: Kindle Store by Trevor Hastie Ó Excellent book I am glad I purchased this book because I need it I work in Machine Learning and AI and needed this book as a reference book I received it on time and in excellent, almost new condition I am very pleased Great book, a must have A lot of online articles quote it out reference it.

Great value and price for this book Only caveat is that it came in with rough corners, and had a kind of dirty sticky back cover The mailing process does not seem to be responsible for this, as the book was well packed It does not affect the actual content of the book, all pages are in great condition Thank you Useful for learning methods for statistical learning.

**books** On The Subject, And It Is Easy To See Why The Book Is Very Well Written, With Informative Graphics On Almost Every Other Page It Looks Great And Inviting You Can Flip The Book Open To Any Page,** read **A Sentence Or Two And Be Hooked For The Next Hour Or So Peter Rabinovitch, The Mathematical Association Of America, May,This Book Describes The Important Ideas In A Variety Of Fields Such As Medicine, Biology, Finance, And Marketingin A Common Conceptual Framework While The Approach Is Statistical, The Emphasis Is On Concepts Rather Than Mathematics Many Examples Are Given, With A Liberal Use Of Colour Graphics It Isa Valuable Resource For Statisticians And Anyone Interested In Data Mining In Science Or Industry The Book S Coverage Is Broad, From Supervised Learning Prediction To Unsupervised Learning The Many Topics Include Neural Networks, Support Vector Machines, Classification Trees And Boosting The First Comprehensive Treatment Of This Topic In Any Book 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 There Is Also A Chapter On Methods For Wide Data P Bigger Than N , Including Multiple Testing And False Discovery RatesFrom The Reviews Like The First Edition, The Current One Is A Welcome Edition To Researchers And Academicians Equally Almost All Of The Chapters Are Revised The Material Is Nicely Reorganized And Repackaged, With The General Layout Being The Same As That Of The First Edition If You Bought The First Edition, I Suggest That You Buy The Second Editon For Maximum Effect, And If You Havent, Then I Still Strongly Recommend You Have This Book At Your Desk Is It A Good Investment, Statistically Speaking Book Review Editor, Technometrics, August , VOL , NOFrom The Reviews Of The Second Edition This Second Edition Pays Tribute To The Many Developments In Recent Years In This Field, And New Material Was Added To Several Existing Chapters As Well As Four New Chapters Were Included These Additions Make This Book Worthwhile To Obtain In General This Is A Well Written Book Which Gives A Good Overview On Statistical Learning And Can Be Recommended To Everyone Interested In This Field The Book Is So Comprehensive That It Offers Material For Several Courses Klaus Nordhausen, International Statistical Review, Vol ,The Second Edition Features AboutPages Of Substantial New Additions In The Form Of Four New Chapters, As Well As Various Complements To Existing Chapters The Book May Also Be Of Interest To A Theoretically Inclined Reader Looking For An Entry Point To The Area And Wanting To Get An Initial Understanding Of Which Mathematical Issues Are Relevant In Relation To Practice This Is A Welcome Update To An Already Fine Book, Which Will Surely Reinforce Its Status As A Reference Gilles Blanchard, Mathematical Reviews, IssueD The Book Would Be Ideal For Statistics Graduate Students This Book Really Is The Standard In The Field, Referenced In Most Papers And **books** On The Subject, And It Is Easy To See Why The Book Is Very Well Written, With Informative Graphics On Almost Every Other Page It Looks Great And Inviting You Can Flip The Book Open To Any Page,** read **A Sentence Or Two And Be Hooked For The Next Hour Or So Peter Rabinovitch, The Mathematical Association Of America, May,This Book Describes The Important Ideas In A Variety Of Fields Such As Medicine, Biology, Finance, And Marketingin A Common Conceptual Framework While The Approach Is Statistical, The Emphasis Is On Concepts Rather Than Mathematics Many Examples Are Given, With A Liberal Use Of Colour Graphics It Isa Valuable Resource For Statisticians And Anyone Interested In Data Mining In Science Or Industry The Book S Coverage Is Broad, From Supervised Learning Prediction To Unsupervised Learning The Many Topics Include Neural Networks, Support Vector Machines, Classification Trees And Boosting The First Comprehensive Treatment Of This Topic In Any Book 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 There Is Also A Chapter On Methods For Wide Data P Bigger Than N , Including Multiple Testing And False Discovery Rates The book that I received was practically like a new one It was a great deal having specially that book with that price.