Consider the following training dataset for classifying the patients based on the type of lens they required in an Eye-Hospital. Assume the hospital requires a system that will predict the lenses for the patients by reading their health status from the table and the system will construct the Decision-Making Tree for prediction. Use the appropriate algorithm to construct the Decision-Making Tree from the training dataset and find the attributes which will be eliminated in the process of making such a classification model.

(Marking Scheme: 2 marks for finding the correct root attribute + 2 marks for finding the correct class attribute + 2 marks for finding the correct attribute(s) to be ignored + 2 marks for the final tree + 2 marks for finding related attributes)

age	spectacle-prescrip	astigmatism	tear-prod-rate	contact-lenses
young	myope	no	reduced	none
young	myope	no	normal	soft
young	myope	yes	reduced	none
young	myope	yes	normal	hard
young	hypermetrope	no	reduced	none
young	hypermetrope	no	normal	soft
young	hypermetrope	yes	reduced	none
young	hypermetrope	yes	normal	hard
pre-presbyopic	myope	no	reduced	none
pre-presbyopic	myope	no	normal	soft
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	myope	yes	normal	hard
presbyopic	hypermetrope	no	reduced	none
presbyopic	hypermetrope	no	normal	soft
presbyopic	hypermetrope	yes	reduced	none
presbyopic	hypermetrope	yes	normal	none

Answer rating: 100% (QA)

To construct the DecisionMaking Tree we can use the ID3 algorithm The steps are as follows Calculate ... View the full answer