Regression Trees with CART: Handling Many-Category Features in R

When building predictive models, categorical features with dozens or hundreds of unique levels often break standard algorithms or lead to overfitting. Understanding how Classification and Regression Trees (CART) mathematically handle these high-cardinality variables is essential for building robust, high-performing models. This course guides you through the underlying mechanics of Sum of Squared Errors (SSE) calculations to find optimal splits without losing predictive power. You will transition from understanding basic decision tree structures to confidently managing complex, multi-level categorical data in your regression workflows. What you'll learn: - Understand the core mathematical principles of CART regression trees and how they evaluate splits. - Calculate Sum of Squared Errors (SSE) for both continuous and high-cardinality categorical predictors. - Implement modern data preprocessing workflows to handle many-category variables effectively. - Configure and train regression tree models in R using the modern tidymodels framework. - Apply cross-validation and hyperparameter tuning to prevent overfitting on complex categories. - Evaluate model performance using robust metrics like RMSE and R-squared. This course starts with foundational concepts of decision trees and the mathematical definition of SSE. You will then progress to practical implementation in R, learning how to prepare your data, structure workflows with tidymodels, and optimize your tree models for real-world datasets. This course is designed for beginner to intermediate data analysts, aspiring data scientists, and R programmers who want to master tree-based modeling. No prior experience with regression trees is required, though a basic familiarity with R programming is helpful. Start mastering regression trees and unlock the power of complex categorical data today.