Data Mining: Practical Machine Learning Tools and Techniques, Second Edition
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- Bu səhifədəki naviqasiya:
- Describing structural patterns
- Table 1.1 The contact lens data.
- Machine learning
Now, finally, we can say what this book is about. It is about techniques for finding and describing structural patterns in data. Most of the techniques that we cover have developed within a field known as machine learning. But first let us look at what structural patterns are. Describing structural patterns What is meant by structural patterns? How do you describe them? And what form does the input take? We will answer these questions by way of illustration rather than by attempting formal, and ultimately sterile, definitions. There will be plenty of examples later in this chapter, but let’s examine one right now to get a feeling for what we’re talking about. Look at the contact lens data in Table 1.1. This gives the conditions under which an optician might want to prescribe soft contact lenses, hard contact lenses, or no contact lenses at all; we will say more about what the individual 6 C H A P T E R 1 | W H AT ’ S I T A L L A B O U T ? Table 1.1 The contact lens data. Spectacle Tear production Recommended Age prescription Astigmatism rate
lenses young
myope no reduced none young
myope no normal soft young
myope yes
reduced none
young myope
yes normal
hard young
hypermetrope no reduced none young
hypermetrope no normal soft young
hypermetrope yes
reduced none
young hypermetrope yes normal
hard pre-presbyopic myope no
none pre-presbyopic myope no
soft pre-presbyopic myope yes
reduced none
pre-presbyopic myope
yes normal
hard pre-presbyopic hypermetrope no reduced none pre-presbyopic hypermetrope no normal soft pre-presbyopic hypermetrope yes
reduced none
pre-presbyopic hypermetrope yes normal
none presbyopic myope no
none presbyopic myope no
none presbyopic myope yes
reduced none
presbyopic myope
yes normal
hard presbyopic hypermetrope no reduced none presbyopic hypermetrope no normal soft presbyopic hypermetrope yes
reduced none
presbyopic hypermetrope yes normal
none P088407-Ch001.qxd 4/30/05 11:11 AM Page 6 features mean later. Each line of the table is one of the examples. Part of a struc- tural description of this information might be as follows: If tear production rate = reduced then recommendation = none Otherwise, if age = young and astigmatic = no then recommendation = soft
Structural descriptions need not necessarily be couched as rules such as these. Decision trees, which specify the sequences of decisions that need to be made and the resulting recommendation, are another popular means of expression. This example is a very simplistic one. First, all combinations of possible values are represented in the table. There are 24 rows, representing three possi- ble values of age and two values each for spectacle prescription, astigmatism, and tear production rate (3 ¥ 2 ¥ 2 ¥ 2 = 24). The rules do not really general- ize from the data; they merely summarize it. In most learning situations, the set of examples given as input is far from complete, and part of the job is to gen- eralize to other, new examples. You can imagine omitting some of the rows in the table for which tear production rate is reduced and still coming up with the rule If tear production rate = reduced then recommendation = none which would generalize to the missing rows and fill them in correctly. Second, values are specified for all the features in all the examples. Real-life datasets invariably contain examples in which the values of some features, for some reason or other, are unknown—for example, measurements were not taken or were lost. Third, the preceding rules classify the examples correctly, whereas often, because of errors or noise in the data, misclassifications occur even on the data that is used to train the classifier. Machine learning Now that we have some idea about the inputs and outputs, let’s turn to machine learning. What is learning, anyway? What is machine learning? These are philo- sophic questions, and we will not be much concerned with philosophy in this book; our emphasis is firmly on the practical. However, it is worth spending a few moments at the outset on fundamental issues, just to see how tricky they are, before rolling up our sleeves and looking at machine learning in practice. Our dictionary defines “to learn” as follows: To get knowledge of by study, experience, or being taught; To become aware by information or from observation; To commit to memory; To be informed of, ascertain; To receive instruction. 1 . 1
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