Paths Subjects Questions Quizzes Pricing Search
Machine Learning Intermediate Pro

Decision Trees

How tree-based models split data and make decisions

20 min read 30 views 1 enrolled

Learn how decision trees partition feature space using impurity measures, how recursive binary splitting works, which hyperparameters control overfitting, and how feature importance is calculated — the foundation for understanding gradient boosting models like LightGBM and XGBoost.

Practice questions (4)

  • Gini Impurity vs Entropy

    Intermediate
    View →
  • Diagnosing and Fixing Overfitting in a Decision Tree

    Intermediate
    View →
  • Feature Importance — MDI Limitations

    Intermediate
    View →
  • Decision Boundary Limitations

    Advanced
    View →

We use cookies for product analytics to improve OmniAtlas. See our Privacy Policy.