Vapnik-Chervonenkis theory

Vapnik-Chervonenkis (VC) theory is a framework in machine learning that measures how well a model can learn from data. It focuses on the concept of a model's capacity to classify different datasets correctly, called the VC dimension. A higher VC dimension means the model can capture more complex patterns but may risk overfitting—modeling noise rather than true patterns. Conversely, a lower VC dimension indicates a simpler model that may underfit. VC theory provides mathematical tools to balance model complexity and accuracy, helping ensure models generalize well to new, unseen data.