A decision tree is a flowchart-like tree structure where an internal node represents a feature(or attribute), the branch represents a decision rule, and each leaf node represents an outcome. It is a supervised learning method used for both classification and regression tasks.

Basics of Decision Trees

  • Decision Nodes: These nodes split the dataset based on an attribute.
  • Leaf Nodes: These nodes represent the final output.
  • Pruning: This is the process of removing nodes that provide little to no value.

Types of Decision Trees

  • Classification Trees: Used for categorical data.
  • Regression Trees: Used for continuous data.

How Decision Trees Work

  1. Start with the entire dataset.
  2. Choose the best attribute to split the data.
  3. Split the data based on the chosen attribute.
  4. Repeat the process for each subset until a stopping criterion is met.

Advantages of Decision Trees

  • Easy to interpret.
  • Non-parametric.
  • Can handle both categorical and numerical data.

Disadvantages of Decision Trees

  • Prone to overfitting.
  • Can be unstable.

Example

Here's an example of a decision tree for a classification problem:

  • Feature: Pet preference
  • Decision Nodes:
    • Do you like dogs?
    • Yes -> Do you like small dogs?
      • Yes -> Small dog
      • No -> Large dog
    • No -> Do you like cats?
      • Yes -> Cat
      • No -> Rabbit

Further Reading

For more information on decision trees, you can check out our Introduction to Machine Learning.

Decision Tree Example