Data Science and Machine Learning (Theory and Projects) A to Z - Overfitting, Underfitting, and Generalization: Regulari

The video discusses overfitting, where a flexible model fits training data too closely, capturing noise and failing to generalize to unseen data. It explains that model flexibility is linked to the number of parameters and features. To avoid overfitting, one can reduce model parameters or apply regularization, which restricts parameter magnitudes. A Python example demonstrates how parameter values affect model flexibility. The video concludes with a preview of evaluating model generalization.

What new insight or understanding did you gain from this video?