Beyond Accuracy: Evaluating ML Model Resilience
Crafting a machine learning model that performs well in a controlled environment is only half the battle. The true test lies in how it generalizes to new, unseen data. Properly evaluating your machine learning model is crucial for understanding its strengths, weaknesses, and ultimately, its real-world performance. Without rigorous evaluation, you risk deploying a model […]



