What is heteroscedasticity in supervised regression?

Heteroscedasticity occurs when variance of errors is not constant across input space.

In regression, one assumption is that errors have constant variance (homoscedasticity). When this assumption is violated, error variance changes with input values. This leads to inefficient estimates and unreliable confidence intervals. Weighted regression or transformations are used to handle heteroscedasticity.