  In the paper, we propose a novel methodology to map learning algorithms on
data (performance map) in order to gain more insights in the distribution of
their performances across their parameter space. This methodology provides
useful information when selecting a learner's best configuration for the data
at hand, and it also enhances the comparison of learners across learning
contexts. In order to explain the proposed methodology, the study introduces
the notions of learning context, performance map, and high performance
function. It then applies these concepts to a variety of learning contexts to
show how their use can provide more insights in a learner's behavior, and can
enhance the comparison of learners across learning contexts. The study is
completed by an extensive experimental study describing how the proposed
methodology can be applied.

Machine Learning