Eager learning methods construct a general and explicit description of target function based on the provided training examples.
Eager learning methods use the same approximation to the target function, which must be learned based on training examples and before input queries are observed
Lazy learning methods simply store the data and generalizing beyond these data is postponed until an explicit request is made.
Lazy learning methods can construct a different approximation to the target function for each encountered query instance.
Suitable for complex and incomplete problem domains, where a complex target function can be represented by a collection of less complex local approximations.
Eager Learning normally requires less space than Lazy Learning does