What is the best plot I can do to understand true values vs errors in prediction in a regression problem? I am facing a regression problem and I would like to understand how my errors distrubuite along the true values I have. For the moment, I have done this scatterplot:

but I want a plot that directly gives me how the error is strong for every true values. A solution could be dividing true values in bins and, for each one, computing a box-plot of the error. Finally, obtaining a plot like this

I wonder if there is a nicer plot and a better way to show that. Maybe something very easy to implement already exist in seaborn but at the moment nothing comes to my mind.
Cheers ;)

Errors will always relate to Y, e.g., low Y will be overpredicted unless $R^2$=1.  So errors are plotted against X or functions of X such as predicted Y.
Binning of continuous variables is always arbitrary and information-losing.
Your plot displays a possible fundamental error in the model formulation: an unmodeled nonlinearity.
