File Size: 16974 KB
Print Length: 600 pages
Publisher: Springer; 2013 edition (May 17, 2013)
Publication Date: May 17, 2013
The two books cover the same extensive subject. If you yahoo "kuhn caret", you will find Max Kuhn's (very informative) presentation of his "caret" R package, and its first slide think that he uses "predictive modeling" as a synonym of "machine learning" - what Hastie and Tibshirani call "statistical learning". Taking on H&T's terminology choice, I will admit both books incorporate theory of "statistical learning" with hands-on illustrations and exercises implemented in R; the get-your-hands-dirty, try-it-out component is, in reality, 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 which includes further items. While ISL is by design very accessible, APM can be more technological - the detail will would be the appreciated by any practitioner - and, as it talks about the various methods, it can and does discuss recent extensions, offering an substantial 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 Ur 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 offering APM five stars, is actually because the book jumped into the catalog setting somewhat too briskly, and delivered on the "applied" promise mostly by identifying "applied" as "illustrated with R examples". If only there were more chapters like Chapter 16, which discusses the very common problem of effective classification in highly unbalanced samples. Nonetheless, I am amazed at "Applied predictive modeling" and recommend it as a practical follow-up, or maybe even alternative, to "Introduction to statistical learning"., This is certainly a fantastic book. I see a lot of mentions to ISL in the comments, but I really feel that this book is an excellent compliment to ISL - specifically for reading after reading ISL - it dives deeper than ISL does into various recent developments but never dives too deeply into overly technical mathematics. This is almost an all natural expansion for supervised learning. I could not recommend this strongly enough., I read Data Science for people who do buiness: What you need to know about data mining and data-analytic thinking before this one which gave a introduction and intuitive feel for data science. This book goes much deeper into the algorithms used in data science. As the title says, this book concentrates on algorithms and models used for prediction, but that covers most apps of data science. This is a great book if you wish to get an understanding of a wide variety of models and how to implement them using Ur. You will want to find another book if you wish to focus on only a few models.
Covers several models
Demonstrates how to use those models in Ur
Contains recommendations for further study
Contains exercises to help practice what is taught
Avoids heavy theoretical mathematics
Expects you to know basic data and some higher-level maths (like matrices), I use an substantial personal library on statistical books which is the best. It is the most effective summaries of complicated data modeling and understanding in the market (together with Lantz's Machine Studying with R). It is not for the weak-hearted and some basics in statistics are needed. Yet , from those with an introductory knowledge in data mining to those that are experts, this book is a jewel. I am unable to reward it enough and the tools that it includes makes Analytics not only easier but also exciting., This is a fantastic book with lots and lots of very useful information. It is not a beginners book, though. This gets 4 stars rather than 5 because I think the book could be better structured which makes it simpler to read. For instance , in the chapters about data pre-processing (beginning of the book) the author uses as model a Support Vector Machine that will only be describe much later in the book.
Likewise, I don't really like the fact that theory is separated from the computing part. One reads all the theory and then once on the computing we need to keep going back and forth to try and remember what because the theory covered by the Ur methods and functions. Making boxes for the processing aspects inside the main kind of text would also make it simpler to read.
But all in all, I think this is a must have book., While this was mainly a review for me, there are always gems to be found in comprehensive texts similar to this. I would have adored to possess this book 6-7 years ago. Although I don't agree with the entirety of the espoused approach (see e. h. " Practical Data Research 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 employing supervised learning models. Within particular, the author's tortue package (which is a perfect friend to this book) supplies a great basis for data-> model pipelining that I would dearly like to see other ML frameworks embrace (scikit learn is near, 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.
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