--- product_id: 1524993 title: "The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition" price: "AED 448" currency: AED in_stock: true reviews_count: 13 url: https://www.desertcart.ae/products/1524993-the-elements-of-statistical-learning-data-mining-inference-and-prediction store_origin: AE region: United Arab Emirates --- # The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition **Price:** AED 448 **Availability:** ✅ In Stock ## Quick Answers - **What is this?** The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition - **How much does it cost?** AED 448 with free shipping - **Is it available?** Yes, in stock and ready to ship - **Where can I buy it?** [www.desertcart.ae](https://www.desertcart.ae/products/1524993-the-elements-of-statistical-learning-data-mining-inference-and-prediction) ## Best For - Customers looking for quality international products ## Why This Product - Free international shipping included - Worldwide delivery with tracking - 15-day hassle-free returns ## Description This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of colour graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorisation, and spectral clustering. There is also a chapter on methods for "wide'' data (p bigger than n), including multiple testing and false discovery rates. Review: There is nothing better - There is no other book I know of in this space with the same combination of thorough detailed math, intuition, application to real-world data, and excellent graphics. It's also very well-written. Their notation can be a bit weird, but whatever. Maybe I'm weird for finding their notation weird. Enough praise. Just buy it and study it. I personally like it better than the comparable books by Barber, Bishop, Murphy, and others, but to each their own. These three are excellent books in their own right, and maybe some would prefer them, especially if one does a lot of Bayesian modeling. But usually, one doesn't. And if you're a beginner in machine learning, my opinion is that studying Bayesian inference as a default can be confusing. Reading advice, if you're not a mathematician (if you are, you don't need my advice): I highly recommend going through a book on standard statistical inference first, else you might be a bit lost, and subtle points that Hastie et al make might be missed (I often pick up details on a second reading - lots of "aha" moments to be had). Not to mention the fact that some of their derivations will seem impenetrable; that one for bias and variance of the linear model in chapter 2 nonplussed me for a while. Luckily there are the accompanying notes by Weatherwax et al (google it), which are seriously helpful. Good options for background are Casella & Berger (the standard), the book "Statistical rethinking from scratch" by Edge (such a good book!), the book "Probability and mathematical statistics" by Meyer (this looks excellent but I don't know it well), and many others (the number of books written on statistical inference asymptotically approaches infinity). Some people like the book by Wasserman but I find it so "skeletal" (as one reviewer said) that one has to go elsewhere for the details anyway. So why not just read a less skeletal book? Anyway, back to ESL. Reading this has made me a less dumb person, even though I've only read in detail the first 3 chapters. I hope it will do the same for you. Review: Actually does something (huge) with the math - I have been using The Elements of Statistical Learning for years, so it is finally time to try and review it. The Elements of Statistical Learning is a comprehensive mathematical treatment of machine learning from a statistical perspective. This means you get good derivations of popular methods such as support vector machines, random forests, and graphical models; but each is developed only after the appropriate (and wrongly considered less sexy) statistical framework has already been derived (linear models, kernel smoothing, ensembles, and so on). In addition to having excellent and correct mathematical derivations of important algorithms The Elements of Statistical Learning is fairly unique in that it actually uses the math to accomplish big things. My favorite examples come from Chapter 3 "Linear Methods for Regression." The standard treatments of these methods depend heavily on respectful memorization of regurgitation of original iterative procedure definitions of the various regression methods. In such a standard formulation two regression methods are different if they have superficially different steps or if different citation/priority histories. The Elements of Statistical Learning instead derives the stopping conditions of each method and considers methods the same if they generate the same solution (regardless of how they claim they do it) and compares consequences and results of different methods. This hard use of isomorphism allows amazing results such as Figure 3.15 (which shows how Least Angle Regression differs from Lasso regression, not just in algorithm description or history: but by picking different models from the same data) and section 3.5.2 (which can separate Partial Least Squares' design CLAIM of fixing the x-dominance found in principle components analysis from how effective it actually is as fixing such problems). The biggest issue is who is the book for? This is a mathy book emphasizing deep understanding over mere implementation. Unlike some lesser machine learning books the math is not there for appearances or mere intimidating typesetting: it is there to allow the authors to organize many methods into a smaller number of consistent themes. So I would say the book is for researchers and machine algorithm developers. If you have a specific issue that is making inference difficult you may find the solution in this book. This is good for researchers but probably off-putting for tinkers (as this book likely has methods superior to their current favorite new idea). The interested student will also benefit from this book, the derivations are done well so you learn a lot by working through them. Finally- don't buy the kindle version, but the print book. This book is satisfying deep reading and you will want the advantages of the printed page (and desertcart's issues in conversion are certainly not the authors' fault). ## Features - This refurbished product is tested and certified to work properly. The product will have minor blemishes and/or light scratches. The refurbishing process includes functionality testing, basic cleaning, inspection, and repackaging. The product ships with all relevant accessories, and may arrive in a generic box. ## Technical Specifications | Specification | Value | |---------------|-------| | Best Sellers Rank | #37,699 in Books ( See Top 100 in Books ) #8 in Data Mining (Books) #17 in Probability & Statistics (Books) #112 in Artificial Intelligence & Semantics | | Customer Reviews | 4.6 out of 5 stars 1,351 Reviews | ## Images ![The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition - Image 1](https://m.media-amazon.com/images/I/517TrzchOML.jpg) ## Customer Reviews ### ⭐⭐⭐⭐⭐ There is nothing better *by E***E on April 28, 2024* There is no other book I know of in this space with the same combination of thorough detailed math, intuition, application to real-world data, and excellent graphics. It's also very well-written. Their notation can be a bit weird, but whatever. Maybe I'm weird for finding their notation weird. Enough praise. Just buy it and study it. I personally like it better than the comparable books by Barber, Bishop, Murphy, and others, but to each their own. These three are excellent books in their own right, and maybe some would prefer them, especially if one does a lot of Bayesian modeling. But usually, one doesn't. And if you're a beginner in machine learning, my opinion is that studying Bayesian inference as a default can be confusing. Reading advice, if you're not a mathematician (if you are, you don't need my advice): I highly recommend going through a book on standard statistical inference first, else you might be a bit lost, and subtle points that Hastie et al make might be missed (I often pick up details on a second reading - lots of "aha" moments to be had). Not to mention the fact that some of their derivations will seem impenetrable; that one for bias and variance of the linear model in chapter 2 nonplussed me for a while. Luckily there are the accompanying notes by Weatherwax et al (google it), which are seriously helpful. Good options for background are Casella & Berger (the standard), the book "Statistical rethinking from scratch" by Edge (such a good book!), the book "Probability and mathematical statistics" by Meyer (this looks excellent but I don't know it well), and many others (the number of books written on statistical inference asymptotically approaches infinity). Some people like the book by Wasserman but I find it so "skeletal" (as one reviewer said) that one has to go elsewhere for the details anyway. So why not just read a less skeletal book? Anyway, back to ESL. Reading this has made me a less dumb person, even though I've only read in detail the first 3 chapters. I hope it will do the same for you. ### ⭐⭐⭐⭐⭐ Actually does something (huge) with the math *by J***T on May 17, 2014* I have been using The Elements of Statistical Learning for years, so it is finally time to try and review it. The Elements of Statistical Learning is a comprehensive mathematical treatment of machine learning from a statistical perspective. This means you get good derivations of popular methods such as support vector machines, random forests, and graphical models; but each is developed only after the appropriate (and wrongly considered less sexy) statistical framework has already been derived (linear models, kernel smoothing, ensembles, and so on). In addition to having excellent and correct mathematical derivations of important algorithms The Elements of Statistical Learning is fairly unique in that it actually uses the math to accomplish big things. My favorite examples come from Chapter 3 "Linear Methods for Regression." The standard treatments of these methods depend heavily on respectful memorization of regurgitation of original iterative procedure definitions of the various regression methods. In such a standard formulation two regression methods are different if they have superficially different steps or if different citation/priority histories. The Elements of Statistical Learning instead derives the stopping conditions of each method and considers methods the same if they generate the same solution (regardless of how they claim they do it) and compares consequences and results of different methods. This hard use of isomorphism allows amazing results such as Figure 3.15 (which shows how Least Angle Regression differs from Lasso regression, not just in algorithm description or history: but by picking different models from the same data) and section 3.5.2 (which can separate Partial Least Squares' design CLAIM of fixing the x-dominance found in principle components analysis from how effective it actually is as fixing such problems). The biggest issue is who is the book for? This is a mathy book emphasizing deep understanding over mere implementation. Unlike some lesser machine learning books the math is not there for appearances or mere intimidating typesetting: it is there to allow the authors to organize many methods into a smaller number of consistent themes. So I would say the book is for researchers and machine algorithm developers. If you have a specific issue that is making inference difficult you may find the solution in this book. This is good for researchers but probably off-putting for tinkers (as this book likely has methods superior to their current favorite new idea). The interested student will also benefit from this book, the derivations are done well so you learn a lot by working through them. Finally- don't buy the kindle version, but the print book. This book is satisfying deep reading and you will want the advantages of the printed page (and Amazon's issues in conversion are certainly not the authors' fault). ### ⭐⭐⭐⭐⭐ excellent overview, especially for outsiders, ties the field together conceptually *by M***O on April 13, 2011* This review is written from the perspective of a programmer who has sometimes had the chance to choose, hire, and work with algorithms and the mathematician/statisticians that love them in order to get things done for startup companies. I don't know if this review will be as helpful to professional mathematicians, statisticians, or computer scientists. The good news is, this is pretty much the most important book you are going to read in the space. It will tie everything together for you in a way that I haven't seen any other book attempt. The bad news is you're going to have to work for it. If you just need to use a tool for a single task this book won't be worth it; think of it as a way to train yourself in the fundamentals of the space, but don't expect a recipe book. Get something in the "using R" series for that. When it came out in 2001 my sense of machine learning was of a jumbled set of recipes that tended to work in some cases. This book showed me how the statistical concepts of bias, variance, smoothing and complexity cut across both fields of traditional statistics and inference and the machine learning algorithms made possible by cheaper cpus. Chapters 2-5 are worth the price of the book by themselves for their overview of learning, linear methods, and how those methods can be adopted for non-linear basis functions. The hard parts: First, don't bother reading this book if you aren't willing to learn at least the basics of linear algebra first. Skim the second and third chapters to get a sense for how rusty your linear algebra is and then come back when you're ready. Second, you really really want to use the SQRRR technique with this book. Having that glimpse of where you are going really helps guide you're understanding when you dig in for real. Third, I wish I had known of R when I first read this; I recommend using it along with some sample data sets to follow along with the text so the concepts become skills not just abstract relationships to forget. It would probably be worth the extra time, and I wish I had known to do that then. Fourth, if you are reading this on your own time while making a living, don't expect to finish the book in a month or two. ## Frequently Bought Together - The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics) - An Introduction to Statistical Learning: with Applications in Python (Springer Texts in Statistics) - Deep Learning (Adaptive Computation and Machine Learning series) --- ## Why Shop on Desertcart? - 🛒 **Trusted by 1.3+ Million Shoppers** — Serving international shoppers since 2016 - 🌍 **Shop Globally** — Access 737+ million products across 21 categories - 💰 **No Hidden Fees** — All customs, duties, and taxes included in the price - 🔄 **15-Day Free Returns** — Hassle-free returns (30 days for PRO members) - 🔒 **Secure Payments** — Trusted payment options with buyer protection - ⭐ **TrustPilot Rated 4.5/5** — Based on 8,000+ happy customer reviews **Shop now:** [https://www.desertcart.ae/products/1524993-the-elements-of-statistical-learning-data-mining-inference-and-prediction](https://www.desertcart.ae/products/1524993-the-elements-of-statistical-learning-data-mining-inference-and-prediction) --- *Product available on Desertcart United Arab Emirates* *Store origin: AE* *Last updated: 2026-05-08*