  Bayesian model averaging (BMA) is the state of the art approach for
overcoming model uncertainty. Yet, especially on small data sets, the results
yielded by BMA might be sensitive to the prior over the models. Credal Model
Averaging (CMA) addresses this problem by substituting the single prior over
the models by a set of priors (credal set). Such approach solves the problem of
how to choose the prior over the models and automates sensitivity analysis. We
discuss various CMA algorithms for building an ensemble of logistic regressors
characterized by different sets of covariates. We show how CMA can be
appropriately tuned to the case in which one is prior-ignorant and to the case
in which instead domain knowledge is available. CMA detects prior-dependent
instances, namely instances in which a different class is more probable
depending on the prior over the models. On such instances CMA suspends the
judgment, returning multiple classes. We thoroughly compare different BMA and
CMA variants on a real case study, predicting presence of Alpine marmot burrows
in an Alpine valley. We find that BMA is almost a random guesser on the
instances recognized as prior-dependent by CMA.
