A decision tree is one of the simplest yet powerful algorithms in machine learning. Think of it as a flowchart where each node asks a question, each branch represents the answer, and the leaf nodes give the final prediction or outcome.
It’s intuitive, easy to visualize, and can handle both classification and regression problems.
How Decision Trees Work
- Start at the root node with the entire dataset.
- Select the feature that best splits the data (based on metrics like Gini impurity or information gain).
- Split the data into branches according to the chosen feature.
- Repeat the process for each branch until you reach a leaf node with the predicted output.
Essentially, the tree keeps asking questions until it “knows” the answer.
Advantages of Decision Trees
- Easy to understand and interpret
- Can handle both numerical and categorical data
- Requires little data preprocessing
- Nonlinear relationships can be modeled easily
Disadvantages
- Can overfit if the tree is too deep
- Sensitive to small changes in data
- Not always the most accurate model compared to ensemble methods
Real-World Examples
- Customer churn prediction: Will a customer leave or stay?
- Loan approval: Approve or reject a loan application
- Medical diagnosis: Predict presence or absence of disease
- Marketing campaigns: Decide which customers to target
Conclusion
Decision trees are a great starting point for machine learning beginners. They are interpretable, versatile, and can be enhanced with ensemble methods like Random Forests for even better accuracy.
Citations
https://savanka.com/category/learn/ai-and-ml/
https://www.w3schools.com/ai/