# Machine Learning Question

1. (Rule-Based Classifier) Consider the decision tree
• Convert the decision tree into a set of classification rule sets
• Are the rules mutually exclusive?
• Is the rule set exhaustive?
• Is ordering needed for this set of rules?
• Do you need a default class for the rule set?

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1. (Nearest Neighbor Classifier) The table below lists a dataset that was used to create a nearest neighbor model that predicts whether it will be a good day to go surfing

 ID Wave Size (Ft) Wave Period (secs) Wind Speed (mph) Good Surf 1 6 15 5 Yes 2 1 6 9 No 3 7 10 4 Yes 4 7 12 3 Yes 5 2 2 10 No 6 10 2 20 No

Assuming that the model uses Euclidean distance to find the k-nearest neighbor, what prediction will the model return for the following query instances when k = 1, 3?

 Wave Size (Ft) Wave Period (secs) Wind Speed (mph) Good Surf 5 6 7 ?

1. (Naïve Bayes Classifier) The table below gives details of symptoms that patients presented and whether they were suffering from

 ID Headache Fever Vomiting Meningitis 1 True True False False 2 False True False False 3 True False True False 4 True False True False 5 False True False True 6 True False True False 7 True False True False 8 True False True True 9 False True False False 10 True False True True

 Headache Fever Vomit Meningitis False False True ?

Using a naïve Bayes classifier to determine whether a patient with the above symptoms have meningitis.

1. (Bayesian Network) Given the Bayesian Network below, what is the joint probability distribution P(Exercise = Yes, Diet = Healthy, Heart Disease = No, Chest Pain = No, Blood Pressure = low)?

1. (Perceptron) Given the perceptron Y = sign(0.4X1+0.3X2+0.7X3-0.5). What is the prediction when X1=0.5, X2=-0.5, X3=0.8?

1. (SVM) Given w = (0.4, 0.3, 0.7) and b = -0.5.
• What is the margin of the SVM?
• What is the prediction when X1=-0.5, X2=-0.5, X3=0.8? (2 points)

1. (Performance Evaluations) Given the confusion matrix
• Calculate the accuracy
• Calculate the F-measure