AI Foundations
How a model learns: under- vs over-fitting
A model that's too simple misses the pattern; one that's too complex memorizes the noise. Add data, add noise, and dial complexity to feel the trade-off that sits underneath all of machine learning.
True pattern Model fit
Good fit
More examples make over-fitting harder.
Real data is messy — noise is what a complex model wrongly memorizes.
Polynomial degree. Low = stiff line; high = wild wiggles.
With enough data and the right complexity, the fit tracks the true curve and generalizes.