A Machine Learning Specialist is attempting to build a linear regression model. Given the displayed residual plot only, what is the MOST likely problem with the model?

A key assumption of linear regression is homoscedasticity, meaning the variance of the residuals should be constant across all levels of the independent variable. If a residual plot shows a fanning-out or fanning-in pattern, it indicates heteroscedasticity (non-constant variance), which implies that linear regression might be an inappropriate model without transformation or a different modeling approach.

While outliers can be observed in residual plots, the most evident problem when variance changes with predicted values is heteroscedasticity, a fundamental violation of linear regression assumptions.

While a zero mean for residuals is generally desirable, the primary issue depicted by a non-constant spread in a residual plot is the violation of the constant variance assumption, not solely the mean.

The residual plot explicitly demonstrates that the residuals do not have constant variance, making this statement incorrect.