  Typically, machine learning models are trained and evaluated without making
any distinction between users (e.g, using traditional hold-out and
cross-validation). However, this produces inaccurate performance metrics
estimates in multi-user settings. That is, situations where the data were
collected by multiple users with different characteristics (e.g., age, gender,
height, etc.) which is very common in user computer interaction and medical
applications. For these types of scenarios model evaluation strategies that
provide better performance estimates have been proposed such as mixed,
user-independent, user-dependent, and user-adaptive models. Although those
strategies are better suited for multi-user systems, they are typically
assessed with respect to performance metrics that capture the overall behavior
of the models and do not provide any performance guarantees for individual
predictions nor they provide any feedback about the predictions' uncertainty.
In order to overcome those limitations, in this work we evaluated the conformal
prediction framework in several multi-user settings. Conformal prediction is a
model agnostic method that provides confidence guarantees on the predictions,
thus, increasing the trustworthiness and robustness of the models. We conducted
extensive experiments using different evaluation strategies and found
significant differences in terms of conformal performance measures. We also
proposed several visualizations based on matrices, graphs, and charts that
capture different aspects of the resulting prediction sets.
