Regression Trees in R with tidymodels

Do you want to harness the power of decision trees for predictive modeling but find traditional R modeling packages disjointed? Building regression trees using the modern tidymodels framework provides a unified, clean, and powerful approach to machine learning in R. By reading this course, you will transition from basic data manipulation to constructing robust Classification and Regression Trees (CART). You will learn how to preprocess data, handle missing values, and make accurate numerical predictions using a consistent syntax. What you'll learn: • Understand the core concepts of regression trees and how decision splits are made • Configure modern tidymodels workflows using parsnip and recipes • Apply regression trees to impute missing numerical values in datasets • Evaluate model performance using metrics like RMSE and R-squared • Tune tree hyperparameters to prevent overfitting • Prepare and split your data effectively using modern resampling techniques. This course begins with foundational concepts of decision trees and the tidymodels ecosystem before guiding you through step-by-step written examples. You will explore data preparation, model training, and performance evaluation through structured text explanations and code snippets. This training is designed for aspiring data analysts and beginners to R who want to build a solid foundation in machine learning. Start learning today and elevate your R programming skills.