# Regression

For regression tasks, the typical accuracy metrics are root mean square error (RMSE) and mean absolute percentage error (MAPE). These metrics measure the distance between the predicted numeric target and the actual numeric answer (ground truth). In Amazon ML, the RMSE metric is used to evaluate the predictive accuracy of a regression model.

Figure 3: Distribution of residuals for a Regression model

It is common practice to review the *residuals* for regression problems. A residual for an observation in the
evaluation data is the difference between the true target and the predicted target.
Residuals represent the
portion of the target that the model is unable to predict. A positive residual indicates
that the model is
underestimating the target (the actual target is larger than the predicted target).
A negative residual
indicates an overestimation (the actual target is smaller than the predicted target).
The histogram of the
residuals on the evaluation data when distributed in a bell shape and centered at
zero indicates that the
model makes mistakes in a random manner and does not systematically over or under
predict any particular
range of target values. If the residuals do not form a zero-centered bell shape, there
is some structure in the
model’s prediction error. Adding more variables to the model might help the model
capture the pattern that
is not captured by the current model.