I wish to better understand the difference between lazy and eager learning. I am having difficulty conceptualising what the "abstraction" refers to between the two.
According to the text book I am reading it says, "The distinction between easy learners and lazy learners is based on when the algorithm abstracts from the data."
A lazy learner delays abstracting from the data until it is asked to make a prediction while an eager learner abstracts away from the data during training and uses this abstraction to make predictions rather than directly compare queries with instances in the dataset.
I understand that KNN algorithm loads all the data into memory so depending on the size of the dataset, the computing requirements could be enormous as well as the fact it's a non-parametric algorithm so there is no training to find parameters, the classification is done on a per query requirement??