  Machine learning has achieved an important role in the automatic
classification of variable stars, and several classifiers have been proposed
over the last decade. These classifiers have achieved impressive performance in
several astronomical catalogues. However, some scientific articles have also
shown that the training data therein contain multiple sources of bias. Hence,
the performance of those classifiers on objects not belonging to the training
data is uncertain, potentially resulting in the selection of incorrect models.
Besides, it gives rise to the deployment of misleading classifiers. An example
of the latter is the creation of open-source labelled catalogues with biased
predictions. In this paper, we develop a method based on an informative
marginal likelihood to evaluate variable star classifiers. We collect
deterministic rules that are based on physical descriptors of RR Lyrae stars,
and then, to mitigate the biases, we introduce those rules into the marginal
likelihood estimation. We perform experiments with a set of Bayesian Logistic
Regressions, which are trained to classify RR Lyraes, and we found that our
method outperforms traditional non-informative cross-validation strategies,
even when penalized models are assessed. Our methodology provides a more
rigorous alternative to assess machine learning models using astronomical
knowledge. From this approach, applications to other classes of variable stars
and algorithmic improvements can be developed.
