Description
-
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 |
-
Test example 1: \you did great”
-
Test example 2: \no students should fail”
-
Did the classi er do what you think it should? If not, why not?
-
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?
1
