A decision tree is the arrangement of the data in a tree structure where, at each node, data is separated to different branches according to the value of the attribute at the node.
To construct a decision tree, we will use a standard ID3 learning algorithm that chooses an attribute that classifies the data samples in the best possible way to maximize the information gain - a measure based on information entropy.
In this chapter, you will learn:
- What a decision tree is and how to represent data in a decision tree in example Swim preference
- In the section Information theory concepts of information entropy and information gain theoretically first, then practically applying on example Swim preference
- ID3 algorithm constructing a decision tree from the training data and its implementation in Python
- How to classify new data items using the constructed decision tree in...