The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Springer Series in Statistics) Hardcover – 2 Sep 2003
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About the Author
Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting.
Top customer reviews
My experience with the book so far if very positive. It contains incredibly relevant machine learning methods/tools which many other books, most notably PRML, doesn't touch upon or at least explain very shortly, which are extensively used in practice. Most notably: Support Vector Machines, Random Forests and Ensemble Learning. Also, the structure of ESL has made a lot more sense to me compared to PRML, it wraps parts of the field into more easily digestible chunks, and therefore makes for a better reference than PRML (just compare the table of contents). Also, as the authors themselves point out, the book itself will rather the reader understands the intuition, algorithm and the cases in which they perform good/bad rather than the mathematical background/proofs behind them (don't worry, most of them are still presented in ESL though). In conclusion, if you can accept the skimming of proof and some rigour in ESL, this book is perfect, and summarizes a large part of the field in such a way that even a mathematically mediocre computer scientist is able to somewhat grasp and apply in real world problems. However, if you want to get the entire picture, you might want to read both ESL and PRML, which will give you some of that Bayesian goodies as well.
beside the formulas and tables there are many figures, in color. these figures give a very good idea how al types of analyses work. After reading a lot of books in this field one can say that this is the best.
My main complaint is about the truly awful print quality (second edition, seventh printing 2013). Many pages look smudged. For at least a dozen pages, the ink has gone right through the paper to make the overleaf pages completely unreadable.
This is a genuinely excellent book. The authors thoroughly understand the topic and explain it with remarkable clarity. There are a good number of well thought out exercises and the book's website contains a lot of additional information and data.
If you really want to understand statistical learning methods, rather than just applying them blindly, then this book is definitely one to read.
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