Regularization: The Art of Keeping Models Humble

Overfitting is the “ego problem” of models. They memorize training data and forget how to generalize.
Regularization is how we humble them.

➡️ L1 (Lasso): Shrinks some weights to zero → performs feature selection.
➡️ L2 (Ridge): Reduces all weights slightly → smooths learning.
➡️ Dropout: Randomly removes neurons during training → prevents co-dependence.

It’s not about punishment but it’s about discipline.
Regularization teaches models to focus on patterns, not exceptions.

💭 Remember: The best models don’t just fit data. They respect uncertainty.