  Reliable uncertainty quantification (UQ) in machine learning (ML) regression
tasks is becoming the focus of many studies in materials and chemical science.
It is now well understood that average calibration is insufficient, and most
studies implement additional methods testing the conditional calibration with
respect to uncertainty, i.e. consistency. Consistency is assessed mostly by
so-called reliability diagrams. There exists however another way beyond average
calibration, which is conditional calibration with respect to input features,
i.e. adaptivity. In practice, adaptivity is the main concern of the final users
of a ML-UQ method, seeking for the reliability of predictions and uncertainties
for any point in features space. This article aims to show that consistency and
adaptivity are complementary validation targets, and that a good consistency
does not imply a good adaptivity. Adapted validation methods are proposed and
illustrated on a representative example.
