Applied Predictive Modeling (Anglais) Relié – 17 mai 2013
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Eric Siegel, Ph.D. Founder, Predictive Analytics World, Author, Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die
Présentation de l'éditeur
This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non-mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics.
Dr. Kuhn is a Director of Non-Clinical Statistics at Pfizer Global R&D in Groton Connecticut. He has been applying predictive models in the pharmaceutical and diagnostic industries for over 15 years and is the author of a number of R packages.
Dr. Johnson has more than a decade of statistical consulting and predictive modeling experience in pharmaceutical research and development. He is a co-founder of Arbor Analytics, a firm specializing in predictive modeling and is a former Director of Statistics at Pfizer Global R&D. His scholarly work centers on the application and development of statistical methodology and learning algorithms.
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.
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Predictive Modeling is a particularly exciting subfield of Data Science. Thanks to the few recent high profile news grabbing success stories (the 2012 US presidential election, the Netflix prize, etc.) it has attracted a lot of attention and prominence. Thanks to the increased use and availability of data in all walks of life we are increasingly able to make reliable predictions and estimates regarding topics and issues that affect us in very substantive ways. This ability may sometimes seem almost magical, but behind it lay some very accessible ideas and techniques. “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. For instance, the way we model as simple of a data as a calendar date can have a significant impact on the kind of analysis and predictive model we choose. This is the kind of information that is often not discussed in other modeling books and can sometimes take years of practical experience before its impact is fully appreciated.Lire la suite ›
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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".
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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This is really the only book I've found that remains clear and understandable while going quite deeply into the theoretical underpinnings of popular predictive modeling techniques. It seems just about everything else out there is highly superficial and skips over the dirty guts of modeling, or is far too complicated and assumes you already have a PhD-level understanding of either stats, math, or computer science. In some ways, Kuhn has done the impossible with this book. Highly recommended.
Each chapter concludes with a "Computing" section, wherein the authors provide R code to accomplish or at least illustrate each step discussed in the chapter. They use public data sets for all their work, so the reader can easily reproduce the exact illustrations used in the chapters. The code chunks are small enough, however, that the readers can easily use them in their own analytics problems. Also, they're not just code listings; there is considerable discussion of the techniques so people less fluent in R can keep up and learn. I found myself sometimes wishing that the R code was interleaved into the chapters, but this minor nit is my only critique of the book (if you could call it a critique), and it may not even be relevant because there are advantages to the "Computing" sections as well, such as ease of using them as reference while working through real-world analyses.
Sometimes I buy a machine learning book and am so overwhelmed by the formulas and formalistic approaches that I wonder if the expense of these kinds of books was worth it. I have no such questions with "Applied Predictive Modeling" - it is clearly worth the cover price. In fact, I'll be recommending it to my employees as a "must have" learning tool and reference book.