What do you think?
Rate this book
Statistical Regression and Classification: From Linear Models to Machine Learning
This text provides a modern introduction to regression and classification with an emphasis on big data and R. Each chapter is partitioned into a main body section and an "extras" section. The main body uses math stat very sparingly and always in the context of something concrete, which means that readers can skip the math stat content entirely if they wish. The "extras" section is for those who feel comfortable with analysis using math stat.
532 pages, Paperback
Published August 1, 2017
6 people are currently reading
17 people want to read
Ratings & Reviews
Friends & Following
Create a free account to discover what your friends think of this book!
Community Reviews
Displaying 1 of 1 review
March 22, 2018
I bought this book sight-unseen, just because I had a very favorable impression of the author's prior work, Art of R Programming. This is what I found:
* Although the R language is not explicitly mentioned in the book's title or subtitle, the book does make heavy use of R code and examples.
* The book is organized in what I would call a "multi-pass" approach. Instead of building up the concepts in order of dependency, it first gives an overview of the space, with plenty of forward references, and then goes back and fills in the details. You may or may not like this approach, but given the choice to do it this way, I think it's well-executed.
* The book assumes that the reader is a programmer, and thus will find code examples a useful form of explication. You probably want to be at least a journeyman-level R programmer in order for this to be helpful for you.
* The treatment of the classical assumptions in linear regression is quite intuitive and highlights the practical importance (or lack thereof).
* The production seems a bit hurried. The computer code is not well typeset, and there are some typos and other errors. The author has been very responsive and is currently putting together some errata on his web page, so I suggest checking that for updates. Search for "matloff regclass"
The copy I got had some printing issues (color illustrations were in B&W) but the author and publisher checked other copies and they don't have this problem, so it seems I just got a lemon and yours will probably be fine. Check your book for misprints when you get it.
Overall a worthwhile read.
* Although the R language is not explicitly mentioned in the book's title or subtitle, the book does make heavy use of R code and examples.
* The book is organized in what I would call a "multi-pass" approach. Instead of building up the concepts in order of dependency, it first gives an overview of the space, with plenty of forward references, and then goes back and fills in the details. You may or may not like this approach, but given the choice to do it this way, I think it's well-executed.
* The book assumes that the reader is a programmer, and thus will find code examples a useful form of explication. You probably want to be at least a journeyman-level R programmer in order for this to be helpful for you.
* The treatment of the classical assumptions in linear regression is quite intuitive and highlights the practical importance (or lack thereof).
* The production seems a bit hurried. The computer code is not well typeset, and there are some typos and other errors. The author has been very responsive and is currently putting together some errata on his web page, so I suggest checking that for updates. Search for "matloff regclass"
The copy I got had some printing issues (color illustrations were in B&W) but the author and publisher checked other copies and they don't have this problem, so it seems I just got a lemon and yours will probably be fine. Check your book for misprints when you get it.
Overall a worthwhile read.
Displaying 1 of 1 review