VC dimension

VC Dimension is a way to measure how complex a machine learning model can be. The VC (Vapnik-Chervonenkis) Dimension is a way of measuring how well a machine learning algorithm can recognize shapes and patterns. To think of it in simpler terms: it is a way to measure how many different shapes the model can learn. An example might be a model that recognizes circles, squares, and triangles. The VC Dimension of this model would be three, because it can recognize three shapes.