Accurate model validation and optimized hyperparameters are essential for delivering reliable machine learning results in high-stakes environments. In this course, Model Validation and Hyperparameter Tuning in R, you’ll gain the ability to improve model performance by implementing robust validation techniques and fine-tuning hyperparameters. First, you’ll explore how to assess model reliability through train-test splits and k-fold cross-validation using R packages like caret and tidymodels. Next, you’ll discover how to identify overfitting and underfitting using performance metrics such as RMSE and AUC. Finally, you’ll learn how to apply grid search, random search, and Bayesian optimization to systematically tune hyperparameters and maximize predictive accuracy. When you’re finished with this course, you’ll have the skills and knowledge of advanced model evaluation and tuning techniques needed to confidently deliver high-performing machine learning models using R.
Overview
Accurate model validation and optimized hyperparameters are essential for delivering reliable machine learning results in high-stakes environments. In this course, Model Validation and Hyperparameter Tuning in R, you’ll gain the ability to improve model performance by implementing robust validation techniques and fine-tuning hyperparameters. First, you’ll explore how to assess model reliability through train-test splits and k-fold cross-validation using R packages like caret and tidymodels. Next, you’ll discover how to identify overfitting and underfitting using performance metrics such as RMSE and AUC. Finally, you’ll learn how to apply grid search, random search, and Bayesian optimization to systematically tune hyperparameters and maximize predictive accuracy. When you’re finished with this course, you’ll have the skills and knowledge of advanced model evaluation and tuning techniques needed to confidently deliver high-performing machine learning models using R.
Taught by
Brian Letort
