In theory courses, VC dimension appears abstract.
But it answers a deep question:
How complex is your model’s decision boundary?
VC dimension measures the largest number of points a model can shatter (perfectly classify in all labelings).
Why this is important❔
Two models with similar parameter counts can have very different capacities.
For example:
📦 k-NN → very high effective capacity
📐 Linear classifier → limited capacity
🌳 Deep trees → extremely high capacity
What you need to understand
Generalization depends on capacity relative to data size.
Too much capacity with little data leads to overfitting.
✅ VC dimension is about expressive power, not just number of parameters.