Question

Advantages of and disadvantages of eager classification (e.G., decision tree) versus lazy classification

Answer 1

Eager classification is seen to be much faster than the lazy classification. This is because it constructs a generalization model before receiving any new tuples to classify. Accuracy of classification is generally seen because weights are assigned to attributes. Since this kind of classification involves a single hypothesis for the entire classification, this leads to decrease in classification levels and also takes a long time. People working on such classifications need to be trained. Lazy classification on the other hand involves a better hypothesis level this improves accuracy of classification .in this classification trading tuples have to be stored increasing costs and involved high end indexing structures. Classifiers are not built into tuples which need to be classified .there could be irrelevant attributes in the data leading to inefficient

Answer 2

• Eager classification is seen to be much faster than the lazy classification.
• This is because it constructs a generalization model before receiving any new tuples to classify.
• Accuracy of classification is generally seen because weights are assigned to attributes.
• Since this kind of classification involves a single hypothesis for the entire classification, this leads to decrease in classification levels and also takes a long time.
• People working on such classifications need to be trained. Lazy classification on the other hand involves a better hypothesis level this improves accuracy of classification .
• in this classification trading tuples have to be stored increasing costs and involved high end indexing structures.
• Classifiers are not built into tuples which need to be classified .
• there could be irrelevant attributes in the data leading to inefficient

hope it helps...(*^▽^*)