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Chapter 5
Similarly, we can convert numerical features to categorical features through a
process called discretization, as we saw in Chapter 4, Recommending Movies Using
Affiity Analysis. We can call any person who is taller than 1.7 m tall, and any person
shorter than 1.7 m short. This gives us a categorical feature (although still an ordinal
one). We do lose some data here. For instance, two people, one 1.69 m tall and one
1.71 m, will be in two different categories and considered drastically different from
each other. In contrast, a person 1.2 m tall will be considered "of roughly the same
height" as the person 1.69 m tall! This loss of detail is a side effect of discretization,
and it is an issue that we deal with when creating models.
In the Adult dataset, we can create a LongHours feature, which tells us if a
person works more than 40 hours per week. This turns our continuous feature
(Hours-per-week) into a categorical one:
adult["LongHours"] = adult["Hours-per-week"] > 40
Creating good features
Modeling, and the loss of information that the simplification causes, are the reasons
why we do not have data mining methods that can just be applied to any dataset. A
good data mining practitioner will have, or obtain, domain knowledge in the area
they are applying data mining to. They will look at the problem, the available data,
and come up with a model that represents what they are trying to achieve.
For instance, a height feature may describe one component of a person, but may
not describe their academic performance well. If we were attempting to predict a
person's grade, we may not bother measuring each person's height.
This is where data mining becomes more art than science. Extracting good features
is difficult and is the topic of significant and ongoing research. Choosing better
classification algorithms can improve the performance of a data mining application,
but choosing better features is often a better option.
In all data mining applications, you should first outline what you are looking for
before you start designing the methodology that will find it. This will dictate the
types of features you are aiming for, the types of algorithms that you can use, and
the expectations on the final result.
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