R Classification Trees: Tune, Evaluate, and Visualize Models

Understanding how machine learning models make decisions and ensuring their reliability is crucial in data science. This course demystifies classification trees, a powerful and interpretable modeling technique. By the end of this course, you'll be able to confidently build, tune, evaluate, and interpret classification tree models in R, making you proficient in a fundamental machine learning skill for various data analysis tasks. What you'll learn: * Understand the foundational concepts of decision trees and classification algorithms. * Apply `tidyverse` principles for efficient data preparation and exploration in R. * Configure and tune classification tree hyperparameters using `tidymodels` workflows. * Evaluate model performance using essential metrics and cross-validation techniques. * Visualize classification tree structures to interpret model decisions and identify key predictors. * Practice building reproducible analysis pipelines for classification tasks in R. The course begins with core decision tree theory, then guides you through practical data preparation, model building, hyperparameter tuning, and comprehensive evaluation, culminating in techniques for model visualization and interpretation. This course is designed for absolute beginners to machine learning and R programming, requiring no prior experience. Start your journey into building interpretable predictive models today.