The Unreasonable Power of Decision Trees in Machine Learning

The Enduring Power of Decision Trees

In an era dominated by large neural networks and LLMs, decision trees remain one of machine learning's most powerful and interpretable tools. MLU-Explain's interactive visualization, which scored 244 points on Hacker News, makes this "unreasonable power" immediately intuitive.

What Is a Decision Tree?

A decision tree is a model that classifies or predicts by combining nested if-else rules in a tree structure. Each internal node represents a question about a specific feature; each leaf node holds a prediction value.

Why "Unreasonably" Powerful?

Decision trees earn their reputation for several reasons:

Interpretability: The decision process is fully transparent — every prediction can be traced to a specific rule path

Minimal preprocessing: Works without feature scaling or normalization

Non-linear boundaries: Naturally handles complex, non-linear decision boundaries

Ensemble foundation: Random Forest, XGBoost, LightGBM — the strongest ensemble methods — are all built on decision trees

Interactive Visualization

MLU-Explain's visualization shows in real time how a decision tree partitions data. Users can adjust tree depth and directly observe the trade-off between overfitting and generalization. It's an excellent resource for both ML beginners and practitioners revisiting fundamentals.