hello1) Bayesian Reasoning –
(a) Consider 10 identical urns of which 9 contain 2 black and 2 white marbles and 1 that contains 5
white and 1 black marble. An urn is picked at Random and a marble is drawn – it turns out to be a
WHITE marble. What is the probability that the marble came from the urn with 5 white marbles?
(b) consider I have 3 pairs of dice : one 6-sided, one 8-sided, and one 12-sided. Tell you I roll a 2.
What is the respective probabilities I used the 6-sided, 8-sided, and 12-sided die?
2. NAÏVE BAYES
Run the Diabetes data using NAÏVE BAYESIAN Classification and 10 fold cross validation. Answer
the following question:
Look at the confusion matrix – how many people were said to test positive for diabetes but DID NOT
(2) Applying Decision Tree Induction
Apply j48 to the Diabetes data supplied with WEKA and use 10-fold cross validation. Then answer
the following questions:
(a) What is the True Positive rate?
(b) What is the False Positive rate?
(c) Look at the confusion matrix. how many people were said to have diabetes BUT did not? (Same
as question 2)
(e) Assume the astigmatism attribute was missing from the 3rd record in the file (raw data). Using
Bayesian techniques, what would you infer should go here?
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