VC Dimension

The VC (Vapnik–Chervonenkis) dimension is a measure of the capacity of a classification model to fit data. It quantifies the largest set of points that the model can perfectly classify in all possible ways. A higher VC dimension indicates a more flexible model that can adapt to complex patterns, but also risks overfitting. Conversely, a lower VC dimension suggests a simpler model that may not capture all nuances but can generalize better. It’s a key concept in understanding the balance between a model's complexity and its ability to perform well on new, unseen data.