  Learning algorithms produce software models for realising critical
classification tasks. Decision trees models are simpler than other models such
as neural network and they are used in various critical domains such as the
medical and the aeronautics. Low or unknown learning ability algorithms does
not permit us to trust the produced software models, which lead to costly test
activities for validating the models and to the waste of learning time in case
the models are likely to be faulty due to the learning inability. Methods for
evaluating the decision trees learning ability, as well as that for the other
models, are needed especially since the testing of the learned models is still
a hot topic. We propose a novel oracle-centered approach to evaluate (the
learning ability of) learning algorithms for decision trees. It consists of
generating data from reference trees playing the role of oracles, producing
learned trees with existing learning algorithms, and determining the degree of
correctness (DOE) of the learned trees by comparing them with the oracles. The
average DOE is used to estimate the quality of the learning algorithm. the We
assess five decision tree learning algorithms based on the proposed approach.
