When considering monomers in radical polymerization, several molecular properties, in the following referred to as 'features' in line with data science terminology, are obvious to consider. It is known from literature that polarity is an important quantity that has direct influence on propagation kinetics. Thus, the dipolar moment of the monomer is of general interest. Note that experimental data is mostly available for bulk polymerization, hence where the polarity of the monomer concomitantly influences radical reactivity and the solvent environment. Further, the length of ester side chains in (meth)acrylates are known to at least indirectly correlate with k p. Thus, molecular weight was added as a further feature. Already when collating experimental data on dipolar moments, it is unfortunately evident that gathering such data is by far not trivial, and generally leads to scattered datasets. To solve this issue, we decided to include calculated data as features in our analysis. To this end, we used the Scheme 1. Overview of the leave one out cross validation (LOOCV) of the regression model, also known as nested cross validation.