Data Mining: Practical Machine Learning Tools and Techniques, Second Edition
Yüklə 4,3 Mb. Pdf görüntüsü
|
- Bu səhifədəki naviqasiya:
- Irises: A classic numeric dataset
- Table 1.4 The iris data.
- CPU performance: Introducing numeric prediction
- Table 1.5 The CPU performance data.
may be associated with the rules themselves to indicate that some are more important, or more reliable, than others. You might be wondering whether there is a smaller rule set that performs as well. If so, would you be better off using the smaller rule set and, if so, why? These are exactly the kinds of questions that will occupy us in this book. Because the examples form a complete set for the problem space, the rules do no more than summarize all the information that is given, expressing it in a different and more concise way. Even though it involves no generalization, this is often a very useful thing to do! People frequently use machine learning techniques to gain insight into the structure of their data rather than to make predictions for new cases. In fact, a prominent and successful line of research in machine learning began as an attempt to compress a huge database of possible chess endgames and their outcomes into a data structure of reasonable size. The data structure chosen for this enterprise was not a set of rules but a decision tree. Figure 1.2 shows a structural description for the contact lens data in the form of a decision tree, which for many purposes is a more concise and perspicuous representation of the rules and has the advantage that it can be visualized more easily. (However, this decision tree—in contrast to the rule set given in Figure 1.1—classifies two examples incorrectly.) The tree calls first for a test on tear
comes. If tear production rate is reduced (the left branch), the outcome is none. If it is normal (the right branch), a second test is made, this time on astigma-
1 4
C H A P T E R 1 | W H AT ’ S I T A L L A B O U T ? normal tear production rate reduced hypermetrope myope none
astigmatism soft
hard none
spectacle prescription yes
no Figure 1.2 Decision tree for the contact lens data. P088407-Ch001.qxd 4/30/05 11:11 AM Page 14
that dictates the contact lens recommendation for that case. The question of what is the most natural and easily understood format for the output from a machine learning scheme is one that we will return to in Chapter 3.
The iris dataset, which dates back to seminal work by the eminent statistician R.A. Fisher in the mid-1930s and is arguably the most famous dataset used in data mining, contains 50 examples each of three types of plant: Iris setosa, Iris versicolor, and Iris virginica. It is excerpted in Table 1.4. There are four attrib- utes: sepal length, sepal width, petal length, and petal width (all measured in cen- timeters). Unlike previous datasets, all attributes have values that are numeric. The following set of rules might be learned from this dataset: If petal length
If sepal width < 2.10 then Iris versicolor If sepal width < 2.45 and petal length < 4.55 then Iris versicolor If sepal width < 2.95 and petal width < 1.35 then Iris versicolor If petal length ≥ 2.45 and petal length < 4.45 then Iris versicolor If sepal length ≥ 5.85 and petal length < 4.75 then Iris versicolor 1 . 2
S I M P L E E X A M P L E S : T H E W E AT H E R P RO B L E M A N D OT H E R S 1 5
Table 1.4 The iris data. Sepal
Sepal width Petal length Petal width length (cm) (cm) (cm)
(cm) Type
1 5.1
3.5 1.4
0.2 Iris setosa 2 4.9 3.0 1.4
0.2 Iris setosa 3 4.7 3.2 1.3
0.2 Iris setosa 4 4.6 3.1 1.5
0.2 Iris setosa 5 5.0 3.6 1.4
0.2 Iris setosa . . .
51 7.0
3.2 4.7
1.4 Iris versicolor 52 6.4 3.2 4.5
1.5 Iris versicolor 53 6.9 3.1 4.9
1.5 Iris versicolor 54 5.5 2.3 4.0
1.3 Iris versicolor 55 6.5 2.8 4.6
1.5 Iris versicolor . . .
101 6.3
3.3 6.0
2.5 Iris virginica 102
5.8 2.7
5.1 1.9
Iris virginica 103
7.1 3.0
5.9 2.1
Iris virginica 104
6.3 2.9
5.6 1.8
Iris virginica 105
6.5 3.0
5.8 2.2
Iris virginica . . .
P088407-Ch001.qxd 4/30/05 11:11 AM Page 15 If sepal width < 2.55 and petal length < 4.95 and petal width < 1.55 then Iris versicolor If petal length ≥ 2.45 and petal length < 4.95 and petal width < 1.55 then Iris versicolor If sepal length ≥ 6.55 and petal length < 5.05 then Iris versicolor If sepal width < 2.75 and petal width < 1.65 and sepal length < 6.05 then Iris versicolor If sepal length ≥ 5.85 and sepal length < 5.95 and petal length < 4.85 then Iris versicolor If petal length ≥ 5.15 then Iris virginica If petal width ≥ 1.85 then Iris virginica If petal width ≥ 1.75 and sepal width < 3.05 then Iris virginica If petal length ≥ 4.95 and petal width < 1.55 then Iris virginica These rules are very cumbersome, and we will see in Chapter 3 how more compact rules can be expressed that convey the same information.
Although the iris dataset involves numeric attributes, the outcome—the type of iris—is a category, not a numeric value. Table 1.5 shows some data for which the outcome and the attributes are numeric. It concerns the relative perform- ance of computer processing power on the basis of a number of relevant attributes; each row represents 1 of 209 different computer configurations. The classic way of dealing with continuous prediction is to write the outcome as a linear sum of the attribute values with appropriate weights, for example: 1 6 C H A P T E R 1 | W H AT ’ S I T A L L A B O U T ? Table 1.5 The CPU performance data. Main
Cycle memory (KB) Cache Channels
time (ns) Min.
Max. (KB)
Min. Max.
Performance MYCT
MMIN MMAX
CACH CHMIN
CHMAX PRP
1 125
256 6000
256 16 128 198 2 29 8000 32000
32 8 32 269 3 29 8000 32000
32 8 32 220 4 29 8000 32000
32 8 32 172 5 29 8000 16000
32 8 16 132 . . .
207 125
2000 8000
0 2 14 52 208
480 512
8000 32 0 0 67 209 480 1000
4000 0 0 0 45 P088407-Ch001.qxd 4/30/05 11:11 AM Page 16 Yüklə 4,3 Mb. Dostları ilə paylaş:
|