Deductive constraints can also be learned directly from data. The next two sections describe more intentional and flexible ways that this might occur, but a method that is already common is to supply additional information that is relevant to the constraint either as a feature or prediction target during training. The earlier example of predicting the properties of molecules that might either be solids or liquids at the prediction temperature provides a useful illustration. If the melting point were supplied as a feature, it would provide a strong deductive constraint for the model to learn whether it was dealing with a solid or liquid. If the melting point were supplied as another prediction target (i.e., another y label) then this is known as a multitask model, and the model would need to internally learn to classify molecules as solids or liquids at a given temperature as a byproduct of predicting the melting point. The latter approach has the benefit that after training the model could be used in scenarios where the melting point is unknown. In both cases, the model is being deliberately provided with information that is relevant to the deductive process and the model learns how to incorporate that information as part of the training process.