• Na ve Bayes
Alice decides to build a na ve Bayes classi er to distinguish between emails from Professor Bob and Pro-fessor Clarence. She has collected the following examples of emails from these two Professors. She uses a bag of words model as features. Compute every parameter for the na ve Bayes classi er using maximum likelihood and classify the nal test examples.
Bob
all students did great on this assignment
Bob
students should come to my o ce
Bob
should you need help talk to the ta
Bob
the ta did great grading this assignment
Clarence
no one did this assignment on time
Clarence
all students should fail
Clarence
the assignment is graded by the ta
1. Test example 1: \you did great"
2. Test example 2: \no students should fail"
3. Did the classi er do what you think it should? If not, why not?
4. Recompute and reclassify the test examples using Laplace smoothing rather than maximum likeli-hood. Did the classi er do what you think it should? If not, why not? Did the classi cations change?
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