There are a large number of supervised machine learning algorithms available. We will examine three of them: decision trees, support vector machines, and Bayesian networks. They all use annotated datasets that contain attributes and a correct response. Typically, a training and a testing dataset is used.
We start with a discussion of decision trees.
A machine learning decision tree is a model used to make predictions. It effectively maps certain observations to conclusions about a target. The term tree comes from the branches that reflect different states or values. The leaves of a tree represent results and the branches represent features that lead to the results. In data mining, a decision tree is a description of data used for classification. For example, we can use a decision tree to determine whether an individual is likely to buy an item based on certain attributes such as income level and postal code.
We want to create a decision tree that...