  Supervised learning techniques typically assume training data originates from
the target population. Yet, in reality, dataset shift frequently arises, which,
if not adequately taken into account, may decrease the performance of their
predictors. In this work, we propose a novel and flexible framework called
DetectShift that quantifies and tests for multiple dataset shifts, encompassing
shifts in the distributions of $(X, Y)$, $X$, $Y$, $X|Y$, and $Y|X$.
DetectShift equips practitioners with insights into data shifts, facilitating
the adaptation or retraining of predictors using both source and target data.
This proves extremely valuable when labeled samples in the target domain are
limited. The framework utilizes test statistics with the same nature to
quantify the magnitude of the various shifts, making results more
interpretable. It is versatile, suitable for regression and classification
tasks, and accommodates diverse data forms - tabular, text, or image.
Experimental results demonstrate the effectiveness of DetectShift in detecting
dataset shifts even in higher dimensions.
