Lazy learning algorithm VS Eager learning algorithm

1.Lazy:

     a.Defers data processing until it recieves a request to classify an unlabeled example.

     b.Replies to a request for information by combining its stored training data.

     c.Discards the constructed answer and any intermediate results.

2.Eager:

     a.Compiles its data into a compressed description or model.

     b.Discards the training data after complication of the model.

     c.Classifies incoming patterns using the induced models,which is retained for future inquests.

3.Trade off:

     a.Lazy algorithms have fewer cumputational costs than eager algorithm during training.

     b.Lazy algorithms have greater storage requirments and higher computation costs on recall.