Developing machine-readable representations that can capture the correlation between structure and reactivity represents a major challenge within computational chemistry. A key consideration when choosing a parameterization method is that, depending on the ML model used, the sparsity of input features (i.e., the location and number of ones and zeros in a bit vector) may influence modelling performance by undermining relevant information within the input vector. MPAA ligands (Figure ), first popularized by Yu and co-workers in 2008, are uniquely favoured ligands for Pd-catalyzed C-H activation as both the carboxylate and amide motifs have relatively weak coordination strengths when compared to phosphine or NHC-type ligands, and both are able to bind with Pd as L-type (neutral) and X-type (anionic) ligands enabling to dynamically adjust between coordination modes throughout the catalytic cycle. Diversity within this ligand-set comes from variation of the α-substituent, and with the acetamide protecting group, enabling a wide range of steric and electronic profiles to be generated. The subtle impact these modifications may have, is somewhat unintuitive and as such, it is thought that ML may be able to aid in predicting reaction outcomes when these ligands are present.