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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