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

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