Applied Machine Learning Using mlr3 in R 1st Edition

Applied Machine Learning Using mlr3 in R (1st Edition) is a practical, modern guide that turns real-world data problems into reliable, reproducible solutions. Whether you’re transitioning from theory to practice or sharpening production-ready skills, this book puts the powerful mlr3 framework and the R ecosystem at the center of your workflow.

Discover step-by-step, hands-on approaches to building, tuning, and validating models with clear explanations and real dataset examples. You’ll learn how to streamline preprocessing and feature engineering, compare algorithms for classification and regression, design robust resampling strategies, and implement efficient hyperparameter tuning — all using the scalable, modular tools of mlr3. Emphasis on interpretability, model evaluation metrics, and reproducibility ensures you can communicate results to stakeholders and deploy models with confidence.

Perfect for data scientists, statisticians, R programmers, and analytics teams across North America, Europe, Asia, and beyond, this book is both a learning resource and a practical reference. Its approachable language and professional focus make advanced techniques accessible without sacrificing rigor, helping you accelerate projects from prototype to production.

Elevate your machine learning practice with a focused guide to machine learning in R using mlr3. Add this essential resource to your library today and start turning complex datasets into actionable insights — whether you’re working in industry, research, or education.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.