Applied Predictive Modeling 1st ed. 2013, Corr. 2nd printing 2018 Edition

by Max Kuhn (Author), Kjell Johnson (Author)

164 ratings

ISBN-13: 978-1461468486

ISBN-10: 1461468485

Why is ISBN important?

Have one to sell?

Sell on Amazon

Due Date: Dec 15, 2020 Rental Details

FREE return shipping at the end of the semester.
Access codes and supplements are not guaranteed with rentals.

In Stock. Rented from Amazon Warehouse , Fulfilled by Amazon

This item cannot be shipped to your selected delivery location. Please choose a different delivery location.

List Price: $109.99
Save:$67.32(61%)

Deliver to Canada

Add to Cart

Used: Good | Details

Sold by apex_media

Fulfilled by Amazon

Access codes and supplements are not guaranteed with used items.

5 used from $74.04

+ $13.28 shipping

Deliver to Canada

In Stock.

Sold by itemspopularsonlineaindemand and Fulfilled by Amazon.

Available at a lower price from other sellers that may not offer free Prime shipping.

Arrives: Monday, Oct 26

List Price: $109.99

Save: $24.74 (22%)

8 new from $85.25

+ $13.28 shipping

Deliver to Canada

Available at a lower price from other sellers that may not offer free Prime shipping.

Winner of the 2014 Technometrics Ziegel Prize for Outstanding Book

Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. Addressing practical concerns extends beyond model fitting to topics such as handling class imbalance, selecting predictors, and pinpointing causes of poor model performance―all of which are problems that occur frequently in practice.

The text illustrates all parts of the modeling process through many hands-on, real-life examples. And every chapter contains extensive R code for each step of the process. The data sets and corresponding code are available in the book's companion AppliedPredictiveModeling R package, which is freely available on the CRAN archive.

This multi-purpose text can be used as an introduction to predictive models and the overall modeling process, a practitioner's reference handbook, or as a text for advanced undergraduate or graduate level predictive modeling courses. To that end, each chapter contains problem sets to help solidify the covered concepts and uses data available in the book's R package.

Readers and students interested in implementing the methods should have some basic knowledge of R. And a handful of the more advanced topics require some mathematical knowledge.

Customers who viewed this item also viewed

Page 1 of 1 Start overPage 1 of 1

This shopping feature will continue to load items when the Enter key is pressed. In order to navigate out of this carousel please use your heading shortcut key to navigate to the next or previous heading.

Gareth James

757

Hardcover
$28.06
Only 2 left in stock - order soon.
Trevor Hastie

478

Hardcover
$75.19
Only 6 left in stock - order soon.
Max Kuhn

24

Hardcover
$79.95
Brett Lantz

94

Paperback
$39.99
Frank E. Harrell Jr.

36

Hardcover
$85.67
Christopher M. Bishop

413

Hardcover
$82.01

More items to explore

Page 1 of 1 Start overPage 1 of 1

Gareth James

757

Hardcover
$28.06
Only 2 left in stock - order soon.
Christopher M. Bishop

413

Hardcover
$82.01
Ian Goodfellow

997

Hardcover
$45.00
Aurélien Géron

991

in Data Processing
Paperback
$44.00
In stock on October 18, 2020.
Trevor Hastie

478

Hardcover
$75.19
Only 6 left in stock - order soon.
Richard McElreath

65

in Geochemistry
Hardcover
$80.83

Special offers and product promotions

Amazon Business : For business-only pricing, quantity discounts and FREE Shipping. Register a free business account

Editorial Reviews

Review

"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)

Review

"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)

Item Weight : 2.2 pounds
Hardcover : 613 pages
ISBN-13 : 978-1461468486
ISBN-10 : 1461468485
Product Dimensions : 6.14 x 1.31 x 9.21 inches
Publisher : Springer; 1st ed. 2013, Corr. 2nd printing 2018 Edition (May 17, 2013)
Language: : English

Best Sellers Rank: #84,264 in Books (See Top 100 in Books)
- #4 in Biostatistics (Books)
- #24 in Mathematical & Statistical Software

Customer Reviews:
164 ratings

Be the first video

Your name here

Customer reviews

5 star		76%
4 star		11%
3 star		6%
2 star		4%
1 star		3%

How are ratings calculated?

Top reviews from the United States

There was a problem filtering reviews right now. Please try again later.

Glass Darkly

The Future is Now (it is with an accurate-enough prediction!)

Reviewed in the United States on January 9, 2018

Verified Purchase

After completing Introduction to Statistical Learning with applications in R, this takes the study of predictive modeling to a new level using the caret package in R. It is so much fun to read and experiment with that I carry it in my backpack, and I read it everywhere (including before going to sleep at night!).

19 people found this helpful

Helpful

Comment

I. Roddis

Wish I'd read this years ago

Reviewed in the United States on December 20, 2016

Verified Purchase

I wish I'd had this book 10 years ago, and the discipline to have sat down and read it thoroughly. It is well written, has beautiful plots that are worthy of a book on visualization all by themselves, has great coverage of topics, and is easy to understand.

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.

The pros:
- 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)

The cons:
- 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.

27 people found this helpful

Helpful

Comment

Amazon Customer

First Rate Case Studies, Modeling Techniques, & R Code

Reviewed in the United States on October 9, 2018

Verified Purchase

My name is Matt. I’m an educator that focuses on data science in business applications. My background is business and mechanical engineering, not computer science. I don’t have a PhD. I’m an ordinary person that fell in love with Data Science. I’ve sense started an education business aimed at bringing applied data science courses to help business-minded people solve real world problems.

I purchased Applied Predictive Modeling after visiting a high performance hedge fund that employs a number of brilliant minds. This book appeared in most of the work spaces so I decided to pick up a copy and read it for myself.

I read the first half of APM on vacation and honestly I couldn’t put it down. The book goes into detail on a wide range of models, many of which I’d never heard of before. Beyond this, APM provides the R code showing exactly how to implement the models. For me, this application focus is valuable.

The book weaves in many case studies from pharmaceuticals, to business, to even using machine learning to find the optimal concrete formula.

I will say that this book is not for complete beginners, but as soon as you get through the basics this is a great book from two of the best minds in modeling. For beginners I recommend R For Data Science.

Hope this helps.

-Matt

9 people found this helpful

Helpful

Comment

Linxing

A concise, useful book for applied predictive modeling

Reviewed in the United States on April 24, 2020

Verified Purchase

As a data scientist, I have read many books in the fields statistics, machine learning and deep learning. But I would say, this book is unparalleled in terms of conciseness and mathematical rigor. Some classics, like the holy bible in ML
"Elements of statistical learning", "Pattern recognition and machine learning" , they illustrate concepts clearly but rarely shed light on real-world application. Some more applied books, like "Data science for business", they give a intuitive way of understanding the concept behind the scene and are great for people who have no previous experience in data science area. For a person who has a master degree in statistics looking for a book that is neither theoretically heavy like ESL nor elementary like Foster & Tom classic, this book is what you need. Besides the clear explanation of a variety of statistical methods in each chapter, the authors guide us walk through a case study to make sense of every single line of code complied in the book.
I've learned quite a lot about data resampling, model tuning just after finishing up the Chapter 5. I am pretty sure I will benefit more as I delve into this book in subsequent chapters. I recommend you spend time reading through this great book and may you have the same feeling as mine.

3 people found this helpful

Helpful

Comment

Dr. Howard B. Bandy

A highly useful and very readable handbook to system development

Reviewed in the United States on June 6, 2018

Verified Purchase

The authors explain that their coverage of predictive modeling includes machine learning, pattern recognition, and data mining, and expands to a broader guide to the process of developing models and quantifying their predictive accuracies.

A major theme throughout the book is detection of overfitting. Techniques to manage overfitting are discussed in detail. These include data preprocessing, normalization, standardization, transformation of distributions, feature selection, train-test split, cross validation, goodness of fit, and error metrics.

Linear and non-linear models are described, with detailed examples of use with actual data.

The illustrations are superb. Fully disclosed code in R is included.

This book is a very readable handbook that I highly recommend to everyone developing predictive models.

6 people found this helpful

Helpful

Comment

Top reviews from other countries

JassiLally

Outstanding book - every analyst should have this in their library

Reviewed in the United Kingdom on April 9, 2017

Verified Purchase

I cannot recommend this book enough; it is outstanding. I honestly wish someone had handed it to me about 2 years ago and told me to read it cover to cover. I am very new to the world of predictive modeling, but I have a decent maths/stats background. I have found the book easy to pick up, but I do think it is a book that needs to be read from start to finish as a lot of the later chapters often referred back to previous examples in earlier chapters. For example, I was keen to just read about classification, but there were many references to earlier chapters on regression. Given this, I went back to read it from the start. So glad I did this as the data pre processing chapter alone is extremely helpful. The types of examples used are also good and well explained (e.g. health, insurance). The examples used in the book are by no means basic (i .e. a lot of pre procesing is required to handle things like missing values) - I am glad the writer has done this because this is reflective of what real data is like...i get annoyed when books often present you with a neat/tidy dataset without telling you how they got to that! I also like the fact it goes into a lot more depth about REAL issues you will face during model building e.g. class imbalance - this is not something I have found is covered well in any other book...and let's be honest, rooting through articles and scholarly papers on the Internet is not ideal if you're pushed for time! The style of writing is also excellent; it's formal, but honestly, I prefer that to a more informal style of writing. There is enough R code at the end of each chapter if you want to do the book's exercises or apply the techniques to your own work. I use this a lot with ISLR and I think they both compliment one another, although, this book covers more advanced stuff. If you are new to predictive modeling, then I would recommend getting both and using them together.

I was reluctant to buy this book, but it has been so worth it- I know I will refer to it in years to come!

2 people found this helpful

B. OSullivan

I find the mathematics is at a level I can comfortably comprehend. The early section on preparing data is ...

Reviewed in the United Kingdom on April 3, 2018

Verified Purchase

I dip in and out of this book regularly when solving problems with machine learning. The techniques, and especially their motivations, are very well described. I find the mathematics is at a level I can comfortably comprehend. The early section on preparing data is really good. That's 80% of the effort involved in getting great regression or classification results.

I also have the elements of statistical learning, which I love, but I'm not yet fluent enough in some of the maths for that book to read as easily as this one.

One person found this helpful

Customer

Realistic data analysis

Reviewed in the United Kingdom on August 25, 2014

Verified Purchase

This books fills a useful gap between the basic "cookbooks" and the more advanced theoretical textbooks. The authors do a good job in taking you through realistic case studies to show the issues involved with data analysis. It definitely helped me get a good feel for both how to apply a range of models and how to use a range of R packages. The book is not perfect and some of the data pre-processing work was too tedious for my liking. It helped to have the extended code handy that is included in the package as it is not the same as that published in the book. The latter has some gaps once a while.

The book pairs well with the Elements of Statistical Learning (which is what the publisher probably attempted) as it addresses similar methods.

I hope that someone writes a similar book but focused on Bayesian machine learning methods.

2 people found this helpful

Joshua Hruzik

All Essentials with R Examples

Reviewed in Germany on December 22, 2019

Verified Purchase

Applied Predictive Modeling by Max Kuhn and Kjell Johnson is a complete examination of essential machine learning models with a clear focus on making numeric or factorial predictions. On nearly 600 pages, the Authors discuss all topics from data engineering, modeling, and performance evaluation.

The core of Applied Predictive Modeling consists of four distinct chapters:
1. General Strategies on how to manipulate and re-sample data.
2. Regression Models for making numeric predictions.
3. Classification Models for making factor predictions.
4. Other Considerations concerning model quality.

Overall, Applied Predictive Modeling is a very informative course on machine learning. It assumes some prior knowledge and might be difficult to access for someone without any knowledge, despite leaving out unnecessary equations (Introduction to Statistical Learning by Robert Tibshirani and Trevor Hastie would be a good read before starting this book.). Some of the book's examples are taken from the field of medicine and pharmaceuticals which make them hard to understand for people outside of the realm of the health sciences.

However, the book does a very good job at making machine learning in R much more systematic. It clearly shows the advantages of using the caret package (written by the book's author) and how to evaluate and tune your model's performance.

If you are not entirely new to data science, this book will yield a high return for you. It makes your process of training a model more straightforward and thorough.

3 people found this helpful

Conrad T.

Carets Package

Reviewed in Australia on December 26, 2018

Verified Purchase

I bought this for the information on the carets package. A lot of work has gone into this book, however it still seems to lack something, or maybe it was because my course is intense 7 weeks long that I never really had a chance to absorb all the material.

If you are looking for modeling tools in R than this book is better than the others out there in terms of practical code to follow. I would have liked some variants of the base Kmeans by Hartigan and Wong to be used with clustering data without class labels, yet my impression is that the authors chose tools that are more commonly used in data science field.

Attached is some of my code, the Summarize.all function was written by a user on a forum but unfortunately I could not find the user who wrote this and as such, I can not properly credit them with the code they wrote. In essence it allows for all metrics to be shown, i.e. AUC, ROC, SPEC, SEN, CI etc. Note, you model may not produce these values and therefore you will get an error.

See all reviews

Pages with related products. See and discover other items: japanese textbook, brand management, regression models, statistics r, systems biology, applied mathematics books

Applied Predictive Modeling 1st ed. 2013, Corr. 2nd printing 2018 Edition

Customers who viewed this item also viewed

More items to explore

Special offers and product promotions

Editorial Reviews

Review

Review

From the Back Cover

About the Author

Product details

Related video shorts (0)

Be the first video

Customer reviews

Customer images

Top reviews from the United States

There was a problem filtering reviews right now. Please try again later.

Top reviews from other countries

Applied Predictive Modeling 1st ed. 2013, Corr. 2nd printing 2018 Edition

Sorry, there was a problem.

Sorry, there was a problem.

P.when("rentBoxJS").execute(function(rentBoxJS){ rentBoxJS.initialize(); }); Rent On clicking this link, a new layer will be open $42.67 On clicking this link, a new layer will be open

Buy used: $74.04

Buy new: $85.25

Customers who viewed this item also viewed

More items to explore

Special offers and product promotions

Editorial Reviews

Review

Review

From the Back Cover

About the Author

Product details

Related video shorts (0)

Be the first video

Customer reviews

Customer images

Top reviews from the United States

There was a problem filtering reviews right now. Please try again later.

Top reviews from other countries

Rent On clicking this link, a new layer will be open

$42.67 On clicking this link, a new layer will be open

Buy used:

$74.04

Buy new:

$85.25