Question

6.8 Compare the advantages and disadvantages of eager classification (e.g., decision tree, Bayesian, neural network) versus lazy classification (e.g., k-nearest neighbor, casebased reasoning). ?

Answer 1

Answer:

hiiiiiiiiiii

take care....................

yeah u can check in Google......

Answer 2

Answer:

Eager learners, when given a set of training tuples, will construct a generalization (i.e., classification) model

before receiving new (e.g., test) tuples to classify. We can think of the learned model as being ready and eager to

classify previously unseen tuples.

Imagine a contrasting lazy approach, in which the learner instead waits until the last minute before doing any

model construction in order to classify a given test tuple. That is, when given a training tuple, a lazy learner simply

stores it (or does only a little minor processing) and waits until it is given a test tuple.