In Figure , the error metrics of the property prediction tasks using the induced SkipSpecies representation are depicted over a range of dimensions of the representation, with different curves representing the different choices of pooling to make the CBFV. Independent of the task, the dimension, and the representation, it is evident that creating a CBFV using max-pooling leads to worse performance for the property prediction tasks. This result likely occurs as not all the information that is present from the constituent species vectors is used in the max pooling operation, whereas sum and mean pooling do use information from all the constituent species vectors. It is possible that the max-pooling operation can, in some cases, neglect the element or species that is most important within a particular composition for the prediction of particular properties. To further expand on this rationale, the max-pooling approach is very sensitive to outliers, as species vectors that may have anomalously high components can dominate the components of the CBFV. Mean-pooling would be less sensitive to outliers due to taking the average over each component, making it more robust to outliers. The resulting max-pooled CBFV as such may not fully describe a composition.