Machine Learning with Hyperparameter Tuning
About
This book offers a practical, hands-on guide to applying a wide range of machine learning algorithms using the R caret package, with a strong emphasis on hyperparameter tuning and model optimization.
Rather than simply listing algorithms, we focus on how to effectively implement, tune, and evaluate models in a systematic way using a unified workflow. Covering both supervised and unsupervised learning methods, this book walks readers through:
- The theoretical foundations and practical motivations behind each algorithm
- Model training and resampling strategies using
caret
- Hyperparameter tuning with grid search and cross-validation
- Performance evaluation and comparison across models
- Fully reproducible code examples using real-world datasets
We explore essential machine learning techniques such as K-Nearest Neighbors, Decision Trees, Support Vector Machines, LASSO and Ridge Regression, Elastic Net, Random Forests, Gradient Boosting, AdaBoost, and XGBoost, all within a consistent and structured modeling framework.
This book is designed for students, researchers, and data practitioners who want to develop a solid understanding of machine learning workflows in R, especially those who aim to go beyond basic modeling and gain insight into how hyperparameter choices influence model performance.
By the end of this book, readers will be able to confidently build, tune, and evaluate predictive models using best practices in machine learning with R.