- Hardcover: 600 pages
- Publisher: Springer; 2013 edition (May 17, 2013)
- Language: English
- ISBN-10: 1461468485
- ISBN-13: 978-1461468486
- Product Dimensions: 6.1 x 1.3 x 9.2 inches
- Shipping Weight: 2.3 pounds (View shipping rates and policies)
- Average Customer Review: 64 customer reviews
- Amazon Best Sellers Rank: #41,881 in Books (See Top 100 in Books)
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Applied Predictive Modeling 2013th Edition
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"There are a wide variety of books available on predictive analytics and data modeling around the web...we've carefully selected the following 10 books, based on relevance, popularity, online ratings, and their ability to add value to your business. 1. Applied Predictive Modeling." (Timothy King, Business Intelligence Solutions Review, solutions-review.com, June, 2015)
"I used this as a supplement in teaching a data science course that I use a range of different resources because I need to cover working with data, model evaluation, and machine learning methods. The next time I teach this course, I will use only this book because it covers all of these aspects of the field." (Louis Luangkesorn, lugerpitt.blogspot.com, June, 2015)
"This is such a good book it has taken me awhile to work through the book. All the while finding examples of why people should read the book...Well thought out examples with the R packages and example code. Take your time and work through this book." (Mary Anne, Cats and Dogs with Data, maryannedata.com, February, 2015)
"This monograph presents a very friendly practical course on prediction techniques for regression and classification models...The authors are recognized experts in modeling and forecasting , as well as developers of R packages and statistical methodologies...It is a well-written book very useful to students and practitioners who need an immediate and helpful way to apply complex statistical techniques." (Stan Lipovetsky, Technometrics, Vol. 56 (3), August, 2014)
"There are hundreds of books that have something worthwhile to say about predictive modeling. However, in my judgment, Applied Predictive Modeling by Max Kuhn and Kjell Johnson (Springer 2013) ought to be at the very top of the reading list ...They come across like coaches who really, really want you to be able to do this stuff. They write simply and with great clarity...Applied Predictive Modeling is a remarkable text...it is the succinct distillation of years of experience of two expert modelers...." (Joseph Rickert, blog.revolutionanalytics.com, June, 2014)
"This strong, technical, hands-on treatment clearly spells out the concepts, and illustrates its themes tangibly with the language R, the most popular open source analytics solution." (Eric Siegel, Ph.D. Founder, Predictive Analytics World, Author, Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die)
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The two books cover the same broad subject. If you google "kuhn caret", you will find Max Kuhn's (very informative) presentation of his "caret" R package, and its first slide will tell you that he uses "predictive modeling" as a synonym of "machine learning" - what Hastie and Tibshirani call "statistical learning". Adopting H&T's terminology choice, I will say that both books combine theory of "statistical learning" with hands-on illustrations and exercises implemented in R; the get-your-hands-dirty, try-it-out element is, in fact, ISL's key difference from the earlier, venerable "Elements of statistical learning".
Both books, inevitably, go over a catalog of statistical-learning techniques. The shorter ISL, in my opinion, is superior at explaining the concepts and communicating the principles, while APM takes the more straightforward approach of "beefing up" the catalog, by spending more pages on each item and including more items. While ISL is by design very accessible, APM can be more technical - the detail will surely be appreciated by any practitioner - and, as it talks about the various methods, it can and does discuss recent extensions, offering an extensive and "fresh" bibliography. R-wise, APM's advantage is not decisive (if you look at content, not line count) but big; the book naturally favors "caret" - which has a useful role, "wrapping" a plethora of third-party R packages, and providing a common interface, plus helpful utilities - but both references and uses the specialist packages as well.
If you are wondering why I am not giving APM five stars, it's because the book jumped into the catalog mode a bit too briskly, and delivered on the "applied" promise mostly by defining "applied" as "illustrated with R examples". I wish there were more chapters like Chapter 16, which talks about the very common problem of effective classification in highly unbalanced samples. Nonetheless, I am impressed by "Applied predictive modeling" and recommend it as a sensible follow-up, or maybe even alternative, to "Introduction to statistical learning".
Covers a wide array of models
Shows you how to use those models in R
Contains references for further study
Contains exercises to help practice what is taught
Avoids heavy theoretical mathematics
Expects you to know basic statistics and some higher level maths (like matrices)
Most recent customer reviews
Overall, a good book.