- Hardcover: 600 pages
- Publisher: Springer; 2013 edition (September 15, 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: 4.7 out of 5 stars See all reviews (62 customer reviews)
- Amazon Best Sellers Rank: #29,341 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)
“…In teaching a data science course…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)
“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)
"Applied Predictive Modeling aims to expose many of these techniques in a very readable and self-contained book. This is a very applied and hands-on book. It guides the reader through many examples that serve to illustrate main points, and it raises possible issues and considerations that are oftentimes overlooked or not sufficiently reflected upon. Highly recommended." (Bojan Tunguz, tunguzreview.com, June 2015)
“This monograph presents a very friendly practical course on prediction techniques for regression and classification models… 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)
“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…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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Top Customer Reviews
For a couple of pharmaceutical guys, (who BTW use R extensively, I've been an analyst in that industry), you'd think the examples would be new chemical or biological entities. Not so! The cases are fun and exciting, ranging from the nontrivial compression strength of concrete (want that bridge to hold when you cross?) to fuel economy, credit scoring, success in grant applications (boy their colleagues will love that one!), and cognitive impairment. I evaluate technology for patents at payroy dot com, and we have a log likelihood model using Bayesian and Monte Carlo that their grant section helped translate seamlessly to R! We're NOT talking pie in the sky pseudo code here, but real life, real results recipes.
The authors talk about the "scholarly veil" -- meaning we general workers and researchers don't always "deserve" to see the underlying process, software and data (and, other than open source, often can't afford it). Wow, do they pop that myth! These authors are relentless in giving every detail, from design and binning to sorting and stacking to ANOVA, regressions, trees, error methods-- the whole ball of wax with live data and live R coding-- all on a shoestring budget! I guarantee you can start with basic stats and run a very well designed predictive model with the methods they detail, without having to pop for SAP/ IBM or SPSS.
One caveat-- even though they don't assume advanced partial differential equations or even probability theory, the R code and methods are at a fast clip. I'd say they are assuming you either have, or will fill in, with R basics and practice or experience. This is NOT a "how to use R" manual, even though it is in a sense-- it is a "how to apply R correctly and robustly in a way that will pass a juried look at your methods and conclusions." Again, REAL WORLD. For comparison, I'd put the math at advanced undergrad and the R at grad level/ professional practice levels. This will make the title excellent both for learning and professional reference. At this writing, the book is hard to find, and being marked up by resellers-- a tribute to its value and demand right out of the gate.
Springer is never cheap, but also never shabby-- the book is typically gorgeous, well edited, combed for errors (the code ran fine on my antique R download-- even though it's free, I'm hesitant to have to learn a new version!), and pedagogically awesome if you're considering this for a class. We recommend books for our library purchasers and of the 25 actively screened in this category (including a focus on prediction, not just data mining), this is in the top three with Hastie above! Highly recommended for research, augmentation, reference, as well as deep study. Lots of insights, too, about where big data, ML, mining and prediction are now and where they are going-- predicting prediction's future.
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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".
There is a natural comparison to be made to The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics). I found this book much, much better. Where ESLII was fractured and seemed to jump from point to point with no explanation, APM proceeded in a well thought-out manner. ESLII used some non-standard notation and assumptions, where APM used notation familiar to anyone with a background in statistics and linear algebra. To be fair, it may be that I'll return to ESL after having read APM and be able to bridge the leaps the authors made with material I've learned from this book.
- Gives a solid introduction to the problem prediction is trying to solve
- Provides a framework for evaluating prediction results, using a consistent data set across all problems.
- Has citations and references for further reading
- Does a good job of contrasting machine learning black-box models and classical statistics' interpertability (see Breiman's Statistical Modeling: Two Cultures paper for some great insights into this phenomenon)
- A bit light on theory, especially proofs and details behind the models. I feel this is a bit of a pro, though, since the citations for the work are provided, and the theorems and proofs are there if you are interested in them.
Most Recent Customer Reviews
I don't have a specific degree in statistics and still was able to get what this...Read more