Model Evaluation: Measuring the True Intelligence of Machines Imagine you’re a teacher evaluating your students after a semester of classes. You wouldn’t just grade them based on one test—you’d look at different exams, assignments, and perhaps even group projects to understand how well they’ve really learned. In the same way, when we train a model, we must evaluate it from multiple angles to ensure it’s not just memorizing but truly learning to generalize. This process is known as Model Evaluation . Why Do We Need Model Evaluation? Training a model is like teaching a student. But what if the student just memorizes answers (overfitting) instead of understanding concepts? Evaluation helps us check whether the model is genuinely “intelligent” or just bluffing. Without proper evaluation, you might deploy a model that looks good in training but fails miserably in the real world. Common Evaluation Metrics 1. Accuracy Analogy : Like scoring the number of correct answers in ...
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