Cross-Validation in R with tidymodels

Evaluating a machine learning model solely on your training data leads to overly optimistic results and poor real-world performance. To build models you can trust, you must master robust validation techniques that accurately measure how your algorithms generalize to unseen data. This text-based course guides you through the core concepts and practical implementation of cross-validation using the modern tidymodels framework in R. You will begin by mastering foundational concepts of data splitting, bias-variance tradeoffs, and the mechanics of resampling. From there, you will progress to writing clean, reproducible R code to partition your data, execute validation folds, and extract reliable metrics. What you'll learn: - Understand the core principles of cross-validation and why it is essential for reliable model evaluation - Partition data effectively using modern tidymodels packages like rsample - Implement k-fold and repeated cross-validation workflows to maximize data utility - Analyze key classification metrics including accuracy, sensitivity, specificity, and ROC AUC - Apply modern R programming best practices, including tidyverse principles and functional programming with purrr - Prevent common validation pitfalls such as data leakage during preprocessing This course is designed for aspiring data scientists, analysts, and R programmers who want to transition from basic model fitting to rigorous model evaluation. No prior experience with machine learning validation is required, though a basic familiarity with R syntax will help you get the most out of the written examples. Start reading today to build more robust, reliable predictive models.