Classification
We will start with the most commonly used machine learning technique, that is, classification. As we reviewed in the first chapter, the main idea is to automatically build a mapping between the input variables and the outcome. In the following sections, we will look at how to load the data, select features, implement a basic classifier in Weka, and evaluate the classifier performance.
Data
For this task, we will have a look at the ZOO
database [ref]
. The database contains 101 data entries of the animals described with 18 attributes as shown in the following table:
animal |
aquatic |
fins |
hair |
predator |
legs |
feathers |
toothed |
tail |
eggs |
backbone |
domestic |
milk |
breathes |
cat size |
airborne |
venomous |
type |
An example entry in the ...
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