What is stochasticity in SGD for Linear Regression?

SGD introduces randomness by using subsets of data for gradient updates.

Instead of computing full gradient, SGD uses one or few samples, creating noisy updates. This noise helps escape shallow local minima and speeds up computation for large datasets.