Decision Tree Learning is a popular machine learning algorithm used for both classification and regression tasks. It works by recursively partitioning the data into subsets based on the values of input features. The decision tree structure resembles a flowchart where each internal node represents a decision based on a particular feature, each branch represents the outcome of that decision, and each leaf node represents the final prediction or decision.
Here’s a comprehensive overview of Decision Tree Learning:
1. Basic Concepts:
i. Root Node:
- The top node in a decision tree, which represents the initial decision or feature.
ii. Internal Node:
- A node that represents a decision based on a feature and leads to further nodes.
iii. Leaf Node (Terminal Node):
- A node that does not split further and provides the final output or decision.
iv. Decision/Leaf Value:
- The outcome or prediction associated with a leaf node.
