Machine Learning with Tree-Based Models in R

The Korshub Take

This beginner-friendly course teaches how to build powerful tree-based models in R using the modern tidymodels ecosystem. You will apply decision trees, random forests, and boosted trees to predict outcomes on health and risk datasets.

Who It's For

R beginners and data analysts looking to build non-linear predictive models using tidymodels and tree ensembles.

Key Takeaways

• tidymodels Framework: Use R's tidymodels package to train, tune, and evaluate tree-based models.
• Random Forests & Gradient Boosting: Harness ensemble techniques like random forests and boosted trees to boost predictive accuracy.
• Real-World Risk Modeling: Practice on health data to predict diabetes incidence and credit risk data to forecast customer churn.