naive bayes classifier tutorial in data mining

Naive bayes classifier in Data Mining

Step 1. Calculate P(Ci)

  •  P(buys_computer = “no”) = 5/14= 0.357
  • P(buys_computer = “yes”)  = 9/14 = 0.643

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Step 2. Calculate P(X|Ci) for all classes

  • P(age = “<= 30” | buys_computer = “no”) = 3/5 = 0.6
  • P(age = “<=30” | buys_computer = “yes”)  = 2/9 = 0.222
  • P(income = “medium” | buys_computer = “no”) = 2/5 = 0.4
  • P(income = “medium” | buys_computer = “yes”) = 4/9 = 0.444
  • P(student = “yes” | buys_computer = “no”) = 1/5 = 0.2
  • P(student = “yes” | buys_computer = “yes) = 6/9 = 0.667
  • P(credit_rating = “fair” | buys_computer = “no”) = 2/5 = 0.4
  • P(credit_rating = “fair” | buys_computer = “yes”) = 6/9 = 0.667

Step 3. Select the scenario against which you want to classify.

  • X = (age <= 30 , income = medium, student = yes, credit_rating = fair)

 Step 4: Calculate P(X|Ci) :

  • P(X|buys_computer = “no”) = 0.6 x 0.4 x 0.2 x 0.4 = 0.019
  • P(X|buys_computer = “yes”) = 0.222 x 0.444 x 0.667 x 0.667 = 0.044

 Step 5: Calculate C P(X|Ci)*P(Ci) :

  • P(X|buys_computer = “no”) * P(buys_computer = “no”) = 0.007
  • P(X|buys_computer = “yes”) * P(buys_computer = “yes”) = 0.028

Therefore,  X belongs to class (“buys_computer = yes”)