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Product details
File Size: 16972 KB
Print Length: 600 pages
Publisher: Springer; 1st ed. 2013, Corr. 2nd printing 2018 edition (May 17, 2013)
Publication Date: May 17, 2013
Sold by: Amazon Digital Services LLC
Language: English
ASIN: B00K15TZU0
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Amazon Best Sellers Rank:
#319,539 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
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!).
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.
There are many fine math-oriented predictive modeling books, such as Hastie (The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)). Kuhn et al consider them "sister texts" and begin immediately to differentiate-- their approach is hands on and practical, for the express purpose of demonstrating HOW to sort, structure and predict via Python or R, for the purpose of accuracy and understanding of the DATA and trends, NOT learning the underlying math.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.Library Picks reviews only for the benefit of Amazon shoppers and has nothing to do with Amazon, the authors, manufacturers or publishers of the items we review. We always buy the items we review for the sake of objectivity, and although we search for gems, are not shy about trashing an item if it's a waste of time or money for Amazon shoppers. If the reviewer identifies herself, her job or her field, it is only as a point of reference to help you gauge the background and any biases.
While this was largely a review for me, there are always gems to be found in comprehensive texts like this. I would have loved to have this book 6-7 years ago. Even though I don't agree with the entirety of the espoused approach (see e.g. "Practical Data Science with R" for an alternative approach to the cross validation/test/train/holdout set), it is a valid one and I highly recommend this to anyone implementing supervised learning models. In particular, the author's caret package (which is a perfect companion to this book) provides a great basis for data->model pipelining that I would dearly love to see other ML frameworks adopt (scikit learn is close, but not quite there), and will provide a practical baseline for those building custom model pipelines and frameworks (or evaluating what is available off the shelf.
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.
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