Introduction to Weka 10 CHAPTER Experience shows that no single machine learning scheme is appropriate to all data mining problems. The universal learner is an idealistic fantasy. As we have empha- sized throughout this book, real datasets vary, and to obtain accurate models the bias of the learning algorithm must match the structure of the domain. Data mining is an experimental science. The Weka workbench is a collection of state-of-the-art machine learning algo- rithms and data preprocessing tools. It includes virtually all the algorithms described in this book. It is designed so that you can quickly try out existing methods on new datasets in flexible ways. It provides extensive support for the whole process of experimental data mining, including preparing the input data, evaluating learning