To develop a chemically interpretable machine learning model, the presence or lack of the topology of collection of atoms (referred to as functional groups) were related to predicted reactivity. The Morgan Fingerprint algorithm was used to represent such functional groups. It avoids the use of manually created functional groups subject to human bias and interpretation. Additionally, previous work indicates that the use of Morgan Fingerprints in machine learning is an effective approach across chemical disciplines. Briefly, this algorithm functions by finding all subgraphs of a molecular graph and assigns a number to these subgraphs calculated via a set of hashing functions applied to each atom and its respective neighborhood. This yields an integer which can be used as a surrogate for the functional group. The size of these subgraphs was determined by a radius parameter that is supplied by the user a priori. Application of a small radius in machine learning has been shown to avoid the potential for the same integer to represent the same functional group, a phenomenon known as a bit collision. In this work, the ability of models trained on different radii were also compared to ensure that the selection of fingerprint radius is optimal for the task at hand.