Given the following dataset (see Appendix) to illustrate your answer. Justify your answer.

1. Discuss the importance of the training set within the classification process. (1 pt)

2. Why k-nearest-neighbor (k-NN) is a lazy learning ? (1 pt)

3. What is the difference between decision tree classification and rule-based classification ? (2 pts)

Do we actually need to apply each machine learning model on the following data and give answer accordingly?

YES

APPENDIX @relation contact-lenses @attribute age {young, pre-presbyopic, presbyopic} @attribute spectacle-prescrip {myope, hypermetrope} @attribute astigmatism {no, yes} @attribute tear-prod-rate {reduced, normal} @attribute contact-lenses {soft, hard, none} @data % 24 instances young, myope, no, reduced, none pre-presbyopic, myope, no, normal, soft young, myope, yes, reduced, none young, myope, yes, normal, hard young, hypermetrope, no, normal, soft presbyopic, myope, yes, normal, hard young, hypermetrope, no, reduced, none young, hypermetrope, yes, reduced, none young, hypermetrope, yes, normal, hard young, myope, no, normal, soft pre-presbyopic, myope, no, reduced, none pre-presbyopic, myope, yes, reduced, none pre-presbyopic, myope, yes, normal, hard pre-presbyopic, hypermetrope, no, reduced, none pre-presbyopic, hypermetrope, no, normal, soft pre-presbyopic, hypermetrope, yes, reduced, none pre-presbyopic, hypermetrope, yes, normal, none presbyopic, myope, no, reduced, none presbyopic, myope, no, normal, none presbyopic, myope, yes, reduced, none presbyopic, hypermetrope, no, reduced, none presbyopic, hypermetrope, no, normal, soft presbyopic, hypermetrope, yes, reduced, none presbyopic, hypermetrope, yes, normal, none