6. What are decision trees and how are they used in machine learning?

Decision trees are a machine learning model used for classification and regression. A decision tree consists of decision nodes, branches, and leaf nodes that represent decisions and their possible outcomes.

How decision trees work:

1. Decision nodes: Represent questions or tests on data attributes.
2. Branches: Represent answers to the questions and lead to the next nodes or leaves.
3. Leaf nodes: Represent final decisions or predicted values.

Example of a decision tree: Suppose we have weather data and want to predict whether to play tennis:

• Is it sunny? (yes/no)
  • Yes: Is humidity high? (yes/no)
    • Yes: Don't play.
    • No: Play.
  • No: Play.

Applications of decision trees:

1. Classification: Predicting categories, such as medical diagnoses.
2. Regression: Predicting numerical values, such as house prices.
3. Decision analysis: Modeling and optimizing decision-making processes.

Decision trees are a popular tool in machine learning due to their simplicity and interpretability.