Explain the decision tree approach with suitable example

The decision tree approach is a popular machine learning technique used for both classification and regression tasks. It is a tree-like model that represents a series of decisions made based on the features of the input data. The decisions are represented as nodes, and the outcomes are represented as leaf nodes.

Here's how a decision tree works:

1. Root Node: The root node is the starting point of the decision tree, and it represents the entire dataset.
2. Internal Nodes: Internal nodes represent a feature or attribute of the dataset, and they are used to split the data based on a specific condition or rule.
3. Branches: Branches represent the possible values or outcomes of the condition or rule applied at each internal node.
4. Leaf Nodes: Leaf nodes are the terminal nodes, and they represent the final _____ ______ _______ ________ __________.
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