I am reading an article about graph neural network and it is mentioned:
In this step, we extract all newly update hidden states and create a final feature vector describing the whole graph. This feature vector can be then used as input to a standard machine learning model.
What does it mean that this feature vector can be used as an input to standard machine learning model? Isnt machine learning all about obtaining the features in the first place? And what does it imply that feature vector would be input to machine learning model? Should it be part of the ML model rather an input?