Introduction
Decision Tree is a popular supervised learning algorithm used for both classification and regression tasks. It uses a tree-like structure where each internal node represents a test on a feature, each branch represents an outcome of the test, and each leaf node represents a class label or output.
Key Concepts
- Root Node: The starting point of the tree.
- Decision Node: Represents a condition or test.
- Leaf Node: Represents the result (class/label).
- Splitting: The process of dividing a node into child nodes based on some feature.
How Decision Tree Works
It recursively selects the best feature that splits the data based on some criterion, usually:
- Gini Index (used in Classification)
- Information Gain (Entropy)
- Variance Reduction (for Regression)
Example
Let’s consider a simple example of predicting whether a person will play tennis based on weather conditions:
| Outlook | Humidity | Wind | Play Tennis |
|---|---|---|---|
| Sunny | High | Weak | No |
| Sunny | High | Strong | No |
| Overcast | High | Weak | Yes |
| Rain | High | Weak | Yes |
| Rain | Normal | Weak | Yes |
| Rain | Normal | Strong | No |
Based on this dataset, the decision tree may look like this:
- If Outlook = Overcast → Play Tennis = Yes
- If Outlook = Sunny and Humidity = High → No
- If Outlook = Sunny and Humidity = Normal → Yes
- If Outlook = Rain and Wind = Weak → Yes
- If Outlook = Rain and Wind = Strong → No
Advantages
- Easy to interpret and visualize
- No need for feature scaling
- Can handle both categorical and numerical data
Disadvantages
- Prone to overfitting
- Can be biased if not properly pruned
Applications
- Customer segmentation
- Loan approval systems
- Medical diagnosis
Conclusion
Decision Trees are powerful yet simple tools for predictive modeling. They work well with small to medium datasets and provide a transparent decision-making process. However, in practical use, ensemble methods like Random Forest or Gradient Boosted Trees are preferred for better performance.