  Copies have been proposed as a viable alternative to endow machine learning
models with properties and features that adapt them to changing needs. A
fundamental step of the copying process is generating an unlabelled set of
points to explore the decision behavior of the targeted classifier throughout
the input space. In this article we propose two sampling strategies to produce
such sets. We validate them in six well-known problems and compare them with
two standard methods. We evaluate our proposals in terms of both their accuracy
performance and their computational cost.
