When an author says Features are the input to Machine Learning Model what does it mean?

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?

Topic gnn neural-network feature-extraction

Category Data Science

1
answered at

There's quite a lot of confusion here:

  • The word "features" is a bit vague but it usually refers to the structured information provided as input to a ML system.
  • No, features are not a part a ML model. They are the input used to train a model.
  • No, ML is not "all about obtaining features". Obtaining the features is just the stage of obtaining the information in a usable way, so it's as if you said that "learning" is all about "obtaining a book".

In this sentence the author means there are (at least) two stages to this ML process: the first step trains a neural network model, then the values of the hidden states of the NN are collected in order to provide them to a second model which uses them as input features.

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