Reference no: EM131077031

Question 1: Bayesian Networks: Metastatic cancer is a possible cause of brain tumors and is also an explanation for increased total serum calcium. In turn, either of these could explain a patient falling into a coma. Severe headache is also associated with brain tumors. A BN representation of this metastatic cancer example is shown below (Figure 1). All the nodes are Booleans. Given that a patient has severe headache, has a brain tumor, not in coma, and does not have symptoms of increased serum calcium, determine the probability that the patient has metastatic cancer.

Question 2: Given the following classification rule on weather data, prune it so that it is not an overfit. The goal is to obtain a good rule whose support is at least 3 and accuracy is 50% or more. The current rule has a support of 1 and accuracy of 100%.

Show your work.

Outlook=sunny and temp=cool and humidity=normal and windy=false ==> Play = Yes

What to submit? Submit a pdf file with your answers via the Blackboard. Your output should look like this:

Question 3: Given the following data, show ways to discretize age based on (i) Equal-width binning (4 bins) (i) Equal frequency binning (4 bins) (iii) Entropy-based discretization. Salary is the outcome class.

Age	Experience	Education	Salary
45	20	MS	High
65	40	6S	Medium
25	5	HS	Low
35	10	6S	High
27	5	BS	High
22	0	BS	Low
.30	3	MS	Medium
66	40	MS	Medium
50	25	BS	Medium
37	15	BS	High
33	10	MS	Medium
40	15	MS	High
23	5	HS	Low
24	2	BS	Medium

Question 4: Transform salary into binary variables using the standard method, the err-correcting code method, and nested dichotomies.