Building Tree-Based Models

Let's put theory into practice and build tree-based classifiers!

Building a Decision Tree

Making Predictions

Building a Random Forest

Sklearn Reference

Practice: Tree Classification

Key Takeaways

• Decision Trees: DecisionTreeClassifier(max_depth=3)
• Random Forests: RandomForestClassifier(n_estimators=100)
• Control overfitting with max_depth parameter
• Random Forests typically outperform single trees
• Use feature_importances_ to understand your model

You've completed Module 6! Next, we'll learn about model evaluation metrics!