Attention:
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QUESTIONS:
Q1: Considering the data provided, explain the need to standardize the attribute values.
R1:
Q2: Explain how you calculated the parameters for standardization and how you used them in the test set.
R2:
Q3: Explain how you calculated the prior probability of an example belonging to a class (the probability before taking into account the attribute values of the example) in your Naïve Bayes classifier implementation. You may include a relevant piece of your code if this helps you explain.
R3:
Q4: Explain how your Naïve Bayes classifier predicts the class to which a test example belongs. You may include a relevant piece of your code if this helps you explain.
R4:
Q5: Explain the effect of the bandwidth parameter on your classifier.
R5:
Q6: Explain what effect the C parameter has on the Logistic Regression classifier.
R6:
Q7: Explain how you determined the best bandwidth and C parameters for your classifier and the Logistic Regression classifier. You may include a relevant piece of your code if this helps you explain.
R7:
Q8: Explain how you obtained the best hypothesis for each classifier after optimizing all parameters.
R8:
Q9: Show the best parameters, the estimate of the true error for each hypothesis you obtained (your classifier and the two provided by the library), the ranges in the expected number of errors given by the approximate normal test, the McNemar test values, and discuss what you can conclude from this.
R9: