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am 30. Juni 2015
“Data Science” is the most exciting research and professional fields these days. It is creating a lot of buzz, both within the academy as well as in the business world. Detractors like to point out that most of the topics and techniques used by people who call themselves Data Scientists have been around for decades if not longer. However, has often been the case that a combination of topics and methodologies becomes important and concrete enough that a truly new subfield emerges.

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.

The book has a fairly low access bar, but it is definitely not intended for a complete novice. It assumes a fairly decent background in statistics, R language, and at least a passing understanding of machine learning. Many of these techniques are covered in this book, but mainly as summaries and refreshers. Each one of them could use up a book of its own, ore even a whole collection of books.

One of the best features of this book is that the authors understand that predictive modeling is not just a bunch of statistical and computational techniques. Understanding the data, how to obtain it, manipulate it, and format it, are some of the most crucial steps for predictive modeling (and other data-driven fields), and are often overlooked and not sufficiently explained in many other books and papers that I have come across. The same can be said about the model selection - the choice of a model and its predictive power will crucially depend on the kind of phenomena that we are predicting, as well as on what exactly are we trying to predict. This book does an excellent job in guiding the reader along these paths and installing the necessary intuitions required for successful predictive modeling. Here too, like with most things in life, there is no substitute for years of experience working with actual real world problems, but going through this book will ensure that you don’t have to stumble too much with your first steps.

Highly recommended.

**** Book provided for review purposes. ****
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am 11. April 2015
Springer's current books have improved remarkably - the layout and content are masterful, this book is a good example. Graphs and code are now in color, and the word "applied" in the title is fully met. The authors cover many relevant topics in predictive modeling, from regression, discriminant analysis, trees & rule-based models. There are relevant and well-constructed practical examples with R code at the end of every chapter, so the theoretical models are immediately implementable. Highly recommended.
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am 20. Februar 2014
Wer sich mit der Vorhersage von Datenreihen beschäftigt, die nicht eben simplen Einflüssen unterliegen - wie fast alles im wirklichen Leben - kommt an Büchern wie diesem kaum vorbei. Der unschätzbare Vorteil von Applied Predictive Modeling ist es aber, nicht für Mathematiker geschrieben zu sein. Das macht es lesbar - und viele der Darstellungen praktisch anwendbar.
Die vorgestellten Methoden sind - gemessen daran, dass es sich um eine allgemeine Darstellung handelt - erstaunlich vollständig; neben klassischen Klassifikations- und Regressionsmodellen wird auch auf Neural Networks, Entscheidungsbäume und Rule Based Systems eingegangen. Und wer sein R zur Hand hat, kann gleich loslegen.
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am 9. Februar 2016
I am learning data science and exploring machine learning. This books is very useful as an hands-on introduction to predictive modeling with many practical examples. Max Kuhn is also author of the caret R-package and the book gives also a good introduction to it. I would recommend complementing this book with The Elements of Statistical Learning of Hastie et al. to understand the theory behind statistical learning.
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