You take a training set, and you end up with a model that is smaller than the size of the training set yet performs well on it (Making the assumption the model is good here).
That is compression. It has nothing to do with what the model is being used for.
You take a training set, and you end up with a model that is smaller than the size of the training set yet performs well on it (Making the assumption the model is good here).
That is compression. It has nothing to do with what the model is being used for.