Do you ever wanted to know more about data mining algorithms? Are you interested in experiences and tricks that are not written in introductory text books? Then, Assessing and Improving Prediction and Classification, by Timothy Masters, is maybe what you need.
This advanced book describes topics such as regression and classification method assessment, resampling and combining classifiers. For each algorithm, the books starts with explanations (some equations and graphs when needed) and continues with corresponding C++ code. It is thus not a book to read from its very beginning until its end. The reader will rather pick some preferred chapters to read.
Key aspects of data mining are discussed such as carefully selecting the test set. One strength of the book is to explain advanced concepts with very few equations. Masters discusses points that are key to all data mining applications, although often not covered in standard textbooks. On the drawback side, one can mention the low quality of the pictures and graphs in the book. In conclusion, Masters’ book is an excellent support for experienced data miners that prefer plain text descriptions rather than mathematical notations.