  In this short note, we provide a sample complexity lower bound for learning
linear predictors with respect to the squared loss. Our focus is on an agnostic
setting, where no assumptions are made on the data distribution. This contrasts
with standard results in the literature, which either make distributional
assumptions, refer to specific parameter settings, or use other performance
measures.
