Classification

We will start with the most commonly used machine learning technique: 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 its performance.

Data

For this task, we will take a look at the ZOO database. The database contains 101 data entries of 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...