Recently, chemists have started to apply machine learning techniques to model various chemical processes for catalyst design , prediction of organic reactions , reactions in a mass spectrometer , and for the analysis of chemical spectra. Moreover, Lai and coworkers have developed machine learning based methodologies to determine molecular features which are responsible for the viscosity behavior and the aggregation of therapeutic antibodies. In short, machine learning is a process where an algorithm is given input variables (x) and an output variable (y). This algorithm then adjusts a set of weights, biases, and other parameters to predict the output variable from the input variables. Given enough weights and an optimized algorithm, the result will be a model that can always predict the output from the inputs. Therefore, one needs to ensure that a given model is robust with high predictive performance on data that was not used to directly train the model. A popular technique for ensuring that this scenario is the case is referred to as k-fold cross validation where the available data is divided into various portions called folds. For example, the available data can be divided into 5-folds each containing 20% of the entire data set. One of these folds is then removed and referred to as the validation set and the remaining training set is used to train the model. The model is then evaluated on the validation set and the performance of the model is calculated. This process is repeated until all the folds has been used as the validation set and statistics can be calculated on how well the model did for the various folds.