Reframing Audience Expansion through the Lens of Probability Density
  Estimation
Claudio Carvalhaes
Audience expansion has become an important element of prospective marketing,
helping marketers create target audiences based on a mere representative sample
of their current customer base. Within the realm of machine learning, a favored
algorithm for scaling this sample into a broader audience hinges on a binary
classification task, with class probability estimates playing a crucial role.
In this paper, we review this technique and introduce a key change in how we
choose training examples to ensure the quality of the generated audience. We
present a simulation study based on the widely used MNIST dataset, where
consistent high precision and recall values demonstrate our approach's ability
to identify the most relevant users for an expanded audience. Our results are
easily reproducible and a Python implementation is openly available on GitHub:
\url{https://github.com/carvalhaes-ai/audience-expansion}