Decision Trees, Random Forests, and XGBoost in R

Tree-based machine learning algorithms are among the most powerful and widely used tools for solving complex business classification and regression problems. To leverage their full potential, you need to understand not just how to run the code, but how to prepare your data, tune your models, and interpret the results. This text-only course guides you from the fundamental principles of decision trees to advanced ensemble techniques like bagging, random forests, and boosting. You will learn to build, tune, and evaluate robust predictive models using modern R programming workflows, ensuring you can confidently apply these techniques to real-world data challenges. What you'll learn: - Understand the foundational concepts of decision trees, entropy, and split criteria. - Apply data preprocessing and cleaning techniques to prepare datasets for modeling in R. - Build and evaluate bagging and random forest models to improve predictive accuracy. - Implement advanced boosting algorithms, including AdaBoost and XGBoost, for high-performance modeling. - Tune model hyperparameters using modern R workflows to prevent overfitting. - Interpret model outputs and feature importance to drive data-informed business decisions.\n\nThe course begins with core definitions and the mechanics of a single decision tree before progressing systematically through ensemble methods, validation strategies, and advanced gradient boosting. Each concept is reinforced with clear written explanations, conceptual breakdowns, and practical R code snippets. This course is designed for beginners, aspiring data analysts, and business professionals looking to build a strong foundation in machine learning. No prior machine learning experience is required, though a basic familiarity with R syntax is helpful. Start reading today to unlock the power of predictive tree-based modeling in R.