What Are Decision Trees?
Decision trees are a type of supervised learning algorithm used for classification and regression. They model decisions and possible consequences as a tree-like structure. Each node represents a feature, each branch a decision rule, and each leaf a possible outcome.
Advantages of Decision Trees
- Simple to understand and interpret. 🌟
- Can handle both numerical and categorical data. 📊
- Requires little data preprocessing. 🔍
Challenges with Decision Trees
Despite their advantages, decision trees can be prone to overfitting, particularly with complex trees. Pruning methods and ensemble methods like Random Forests can help address these issues.
Resources
Explore more about decision trees and related techniques: