Overfitting vs Underfitting 🎯

Why do ML models fail? Usually because of one of these two villains:

Overfitting: The model memorizes training data but fails on new data. (Like a student who memorizes past exam questions but can’t handle a new one.)

Underfitting: The model is too simple to capture patterns. (Like using a straight line to fit a curve.)

The sweet spot? A model that generalizes well.

Note: Regularization, cross-validation, and more data usually help fight these problems.