# How to detect noisy datasets (bias and variance trade-off)

expected loss = bias + variance + noise

I understand that we minimize this quantity by finding the "best" balance between low bias/high variance and high bias/low variance. However, the noise term is beyond our control. So in a sense, if noise is large, then learning is pointless, right? Are there techniques for detecting when this might be the case?