  Machine-learning models are ubiquitous. In some domains, for instance, in
medicine, the models' predictions must be interpretable. Decision trees,
classification rules, and subgroup discovery are three broad categories of
supervised machine-learning models presenting knowledge in the form of
interpretable rules. The accuracy of these models learned from small datasets
is usually low. Obtaining larger datasets is often hard to impossible.
Pedagogical rule extraction methods could help to learn better rules from small
data by augmenting a dataset employing statistical models and using it to learn
a rule-based model. However, existing evaluation of these methods is often
inconclusive, and they were not compared so far. Our framework PRELIM unifies
existing pedagogical rule extraction techniques. In the extensive experiments,
we identified promising PRELIM configurations not studied before.
