Decision Tree Models

Introduction to Decision Tree Models

Decision tree models are a family of machine learning algorithms that use tree-like structures to make decisions or predictions. They are popular for both classification (predicting categories) and regression (predicting numbers).

Why use tree-based models?

Easy to understand and visualize
Can handle both numerical and categorical data
Require little data preparation
Work for both regression and classification

Types of Decision Tree Models

Model	When to Use	Key Feature
Decision Tree	Simple, interpretable models; small/medium data	Easy to visualize, can overfit
Random Forest	More accuracy, less overfitting	Many trees, averages predictions
Extra Trees	Faster, more randomness, robust	Random splits, very fast
Gradient Boosting	Highest accuracy, complex data	Builds trees sequentially, sensitive

Key Differences

Decision Tree: One tree, easy to interpret, can overfit.
Random Forest: Many trees, less overfitting, more robust.
Extra Trees: Like random forest but splits are more random, often faster.
Gradient Boosting: Trees built one after another, each fixing the last, very accurate but can be sensitive to settings.

When to Use Each Model

Use Decision Tree for simple, interpretable models or when you need to explain decisions.
Use Random Forest for better accuracy and robustness, especially with lots of features.
Use Extra Trees for speed and when you want even more randomness.
Use Gradient Boosting for the highest accuracy, especially in competitions or complex problems.

Visual Comparison

Summary Table

Model	Handles Overfitting	Fast to Train	Best for Accuracy	Feature Importance
Decision Tree	No	Yes	No	Yes
Random Forest	Yes	Medium	Yes	Yes
Extra Trees	Yes	Yes	Yes	Yes
Gradient Boosting	Yes	No	Yes (if tuned)	Yes

Use this page as a quick guide to choose the right tree-based model for your problem!