Activity equations trying to mimic experimental catalytic performance derived from reaction profiles and microkinetic models have been the state of the art in modeling in the last decades. This approach has been able to reproduce semiquantitatively activity volcano plots leading to successful catalyst optimization through the use of descriptors. As systems become more complex (both catalysts and reactants), these methods face increasing limitations. Statistical Learning (SL) techniques can overcome these limitations and improve the search for descriptor-based performance equations. However, the black-box nature of SL techniques makes physical interpretation of the so-obtained models difficult. To advance in the integration of these methodologies to real problems, we have merged experimental activity and selectivity presented as a function of chemical descriptors from Density Functional Theory for the catalyzed hydrodehalogenation of CH2X2 (for X = Br, Cl) leading to three main products. The employed Bayesian procedure is able to identify robust equations for activity and selectivity as a function of only two descriptors. This work provides a starting point to solve complex reaction networks using a set of statistical learning tools and hybrid data.