Results for "complexity"

Measures a model’s ability to fit random noise; used to bound generalization error.

Rademacher Complexity is a way to measure how well a learning model can adapt to random patterns in data. Imagine you have a set of points and you randomly assign labels to them, like flipping a coin for each point. Rademacher Complexity helps us understand how well a model can fit those random l...