In the previous article in this series, we showed that common ensemble models like random forest and gradient boosting are *uncalibrated*: they are not guaranteed to estimate aggregates or rollups of the data in an unbiased way. However, they can be preferable to calibrated models such as linear or generalized linear regression, when they make more accurate predictions on individuals. In this article, we’ll demonstrate one ad-hoc method for calibrating an uncalibrated model with respect to *specific* grouping variables. This "polishing step" potentially returns a model that estimates certain rollups in an unbiased way, while retaining good performance on individual predictions.

Continue reading An Ad-hoc Method for Calibrating Uncalibrated Models