: Data preparation and visualization within a popular software tool
Most books about a particular software tool will focus on the core capabilities of that tool, giving only cursory (if any) coverage about the housekeeping or preparatory functions. For example, most books I’ve read that discuss RapidMiner (and there aren’t many) spend 90% of their time on Operators and how to build a Process. That’s great, but there are steps that can and should be taken before ever getting to that point.
That’s where this book shines. You won’t find much here about linear regression or k-means or support vector machines. Rather, you’ll find plenty about looking at data, cleaning it, and visualization.
In a recent tutorial on using RapidMiner, I deliberately took the reader down a path where we encountered common errors in using the k-means Operator. [Read more…] about Book Review: Exploring Data with RapidMiner