Lazy learners store all their training data until a training data set forms. They often work with data sets that may be updated continually and become outdated frequently. By waiting to process data until there's a whole data set, they can learn faster and are ideal for working with large, changing data sets with a smaller set of attributes.
Eager learners, on the other hand, are ready to learn before class even begins. By constructing a classification system before testing, the eager learning algorithms are already prepared to sort data as it comes in. It takes longer to train these systems, but they are excellent for making accurate predictions based on data from multiple sources.