  Ensuring a reliable communication in wireless networks strictly depends on
the effective estimation of the link quality, which is particularly challenging
when propagation environment for radio signals significantly varies. In such
environments, intelligent algorithms that can provide robust, resilient and
adaptive links are being investigated to complement traditional algorithms in
maintaining a reliable communication. In this respect, the data-driven link
quality estimation (LQE) using machine learning (ML) algorithms is one of the
most promising approaches. In this paper, we provide a quantitative evaluation
of design decisions taken at each step involved in developing a ML based
wireless LQE on a selected, publicly available dataset. Our study shows that,
re-sampling to achieve training class balance and feature engineering have a
larger impact on the final performance of the LQE than the selection of the ML
method on the selected data.
