ML.NET Model Evaluation: Measuring and Improving Accuracy

Building a machine learning model is only half the battle; knowing how to accurately measure its performance is what separates successful applications from costly failures. This text-only course guides you through the essential concepts of model evaluation within the .NET ecosystem. You will transition from simply training models to systematically assessing their quality, understanding exactly when a model is ready for production and how to prevent common pitfalls like overfitting. Through clear written explanations and code examples, you will learn to: 1. Understand foundational machine learning metrics, including regression, classification, and clustering evaluation. 2. Compare micro-accuracy and macro-accuracy to choose the right performance indicators for imbalanced datasets. 3. Apply cross-validation techniques in ML.NET to ensure stable and generalizable model performance. 4. Identify and mitigate overfitting and underfitting using modern validation strategies. 5. Leverage AutoML evaluation capabilities within ML.NET to streamline your model assessment pipeline. 6. Implement modern C# coding patterns to log, track, and analyze evaluation metrics efficiently. The course begins with core terminology and foundational concepts of model performance before moving into practical, step-by-step written guides that show you how to write clean evaluation code in C#. Designed for .NET developers and beginner machine learning enthusiasts who have a basic grasp of C# and want to master the critical step of model assessment, this course requires no advanced data science background. Start reading today to build reliable, high-performing machine learning models in .NET.