The Splendor of Decision Trees 🌳

What is a Decision Tree?

A decision tree is a flowchart-like structure that is used for making decisions or for classification tasks. In a tree, each node represents a test on an attribute, each branch represents the outcome of the test, and each leaf node represents a class label (decision taken after computing all attributes).

Why Use Decision Trees?

• Easy to understand and interpret.
• Requires little data preparation.
• Can handle both numerical and categorical data.
• Useful for feature selection.

How Decision Trees Work 🌟

Decision trees use a top-down, recursive approach known as recursive partitioning. The algorithm splits the data into subsets that contain instances with similar values (homogeneous). This process is repeated on each derived subset in a top-down, recursive manner until it is not possible to split further.