Data Mining: Practical Machine Learning Tools and ... - LIDeCC

1.2 SIMPLE EXAMPLES: THE WEATHER PROBLEM AND OTHERS 9computers. To decide whether something has actually learned, you need to seewhether it intended to or whether there was any purpose involved. That makesthe concept moot when applied to machines because whether artifacts can behavepurposefully is unclear. Philosophic discussions of what is really meant by “learning,”like discussions of what is really meant by “intention” or “purpose,” arefraught with difficulty. Even courts of law find intention hard to grapple with.Data miningFortunately, the kind of learning techniques explained in this book do notpresent these conceptual problems—they are called machine learning withoutreally presupposing any particular philosophic stance about what learning actuallyis. Data mining is a practical topic and involves learning in a practical, nota theoretical, sense. We are interested in techniques for finding and describingstructural patterns in data as a tool for helping to explain that data and makepredictions from it. The data will take the form of a set of examples—examplesof customers who have switched loyalties, for instance, or situations in whichcertain kinds of contact lenses can be prescribed. The output takes the form ofpredictions about new examples—a prediction of whether a particular customerwill switch or a prediction of what kind of lens will be prescribed under givencircumstances. But because this book is about finding and describing patternsin data, the output may also include an actual description of a structure thatcan be used to classify unknown examples to explain the decision. As well asperformance, it is helpful to supply an explicit representation of the knowledgethat is acquired. In essence, this reflects both definitions of learning consideredpreviously: the acquisition of knowledge and the ability to use it.Many learning techniques look for structural descriptions of what is learned,descriptions that can become fairly complex and are typically expressed as setsof rules such as the ones described previously or the decision trees describedlater in this chapter. Because they can be understood by people, these descriptionsserve to explain what has been learned and explain the basis for new predictions.Experience shows that in many applications of machine learning todata mining, the explicit knowledge structures that are acquired, the structuraldescriptions, are at least as important, and often very much more important,than the ability to perform well on new examples. People frequently use datamining to gain knowledge, not just predictions. Gaining knowledge from datacertainly sounds like a good idea if you can do it. To find out how, read on!1.2 Simple examples: The weather problem and othersWe use a lot of examples in this book, which seems particularly appropriate consideringthat the book is all about learning from examples! There are several