Introduction
Decision Trees are a fundamental algorithm in machine learning used for both classification and regression tasks. 🧠 They work by splitting data into subsets based on feature values, creating a tree-like structure where each internal node represents a decision, and each leaf node represents an outcome.
decision tree structure
Figure: Basic structure of a decision tree
How It Works
- Root Node: Start with the entire dataset.
- Splitting: Select the best feature to split the data (e.g., using Gini impurity or information gain).
- Recursive Partitioning: Repeat the process on each subset until all data points are classified or a stopping criterion is met.
- Leaf Nodes: Final decisions are made at the leaves.
decision tree splitting
Figure: Splitting data based on feature values
Applications
- Customer Segmentation 🎯
- Medical Diagnosis 🩺
- Predictive Analytics 📊
- Game Theory 🎮
customer segmentation
Figure: Decision trees in customer segmentation
Advantages and Disadvantages
✅ Advantages:
- Easy to understand and interpret.
- Requires little data preprocessing.
- Handles both numerical and categorical data.
❌ Disadvantages:
- Prone to overfitting.
- Not suitable for large datasets.
- Sensitive to small changes in data.
Further Reading
For deeper insights, explore our machine learning overview or decision trees example. 📘