Data Mining: Practical Machine Learning Tools and Techniques, Second Edition

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.

CPU performance: Introducing numeric prediction

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:

16 CHAPTER 1| WHAT’S IT ALL ABOUT?

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