In chemistry, often ML models are created for a specific task and could not be applied to similar yet different datasets without loss of accuracy/precision/recall. However, model transferability could be understood in various ways: (i) proxy learning, when a more specific problem was solved and the solution was applied (including the dataset and features) to solve a more general problem; (ii) extrapolation, when model was built on a similar dataset, applied on a different dataset, which is too different to be considered as validation/test. A proxy learning example is shown on Figure , when the same model results are plotted with structure type yvector (9 classes), but the color-coded output is based on crystallographic compound classes (7 classes), where some structures are structurally similar and often grouped together. For example, TlI and FeB, which are both distorted variations of NaCl, and require advanced tools to be separated with materials informatics. These two structure types were segregated successfully with SVM and XGBoost, but no separation was observed with PLS-DA (Figure ). In this case (Figure ), proxy learning performed statistically even better than direct learning (Figure ), specifically because of the structural similarities of these structure types. On the other hand, a different case of a possible proxy learning, the problem of centrosymmetric vs. noncentrosymmetric structure classification (two classes), Figure , remains unsolved, which is a more typical outcome for proxy learning. The other case of model transferability is applying a new dataset to the model with already solved problem. Almost a decade ago we tackled AB binary equiatomic classification for the first time, with experimental validation. A small dataset (yet, significantly larger than structure mapping approach used prior to that work) was used which resulted in a decent separation of structure types. It is important to note that the structure types used in that study differ from the ones used in the current study. No matter if the full feature set (56) or feature-selected set (33) was used, the separation of the classes was not significantly different from any other composition featurizer. This is consistent with our expectations; none of the structure types were segregated within the LV space (Figure ).