You create a regression model with low RMSE and review the best model metrics. Where on the Residual histogram should the most frequently occurring residual values cluster for your model?

Residuals clustering around 1 would indicate a systematic positive bias in the predictions, meaning the model consistently underestimates the actual values by approximately one unit.

Residuals clustering around 0.5 would indicate a systematic positive bias, where the model consistently underestimates the actual values by about 0.5 units.

In a well-performing regression model with low Root Mean Squared Error (RMSE), the residuals, which are the differences between predicted and actual values, should ideally be small and randomly distributed around zero, indicating accurate predictions without systematic bias.

Residuals clustering around -1 would indicate a systematic negative bias in the predictions, meaning the model consistently overestimates the actual values by approximately one unit.

Residuals clustering around 2 would indicate a significant systematic positive bias, where the model consistently underestimates the actual values by approximately two units.

Residuals clustering around -0.5 would indicate a systematic negative bias, where the model consistently overestimates the actual values by about 0.5 units.