Naive bayes classifier in Data Mining
Step 1. Calculate P(C_{i})
P(buys_computer = “no”) = 5/14= 0.357.
P(buys_computer = “yes”) = 9/14 = 0.643.
Step 2. Calculate P(XC_{i}) 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(XCi) :
P(Xbuys_computer = “no”) = 0.6 x 0.4 x 0.2 x 0.4 = 0.019
P(Xbuys_computer = “yes”) = 0.222 x 0.444 x 0.667 x 0.667 = 0.044
Step 5: Calculate C P(XCi)*P(Ci) :
P(Xbuys_computer = “no”) * P(buys_computer = “no”) = 0.007
P(Xbuys_computer = “yes”) * P(buys_computer = “yes”) = 0.028
Next Similar Tutorials

Bayesian Networks MCQs  Artificial Intelligence
 Decision tree induction on categorical attributes
 Decision Tree Induction and Entropy in data mining – Click Here
 Overfitting of decision tree and tree pruning – Click Here
 Attribute selection Measures – Click Here
 Computing InformationGain for ContinuousValued Attributes in data mining – Click Here
 Gini index for binary variables – Click Here
 Bagging and Bootstrap in Data Mining, Machine Learning – Click Here
 Evaluation of a classifier by confusion matrix in data mining – Click Here
 Holdout method for evaluating a classifier in data mining – Click Here
 RainForest Algorithm / Framework – Click Here
 Boosting in data mining – Click Here
 Naive Bayes Classifier – Click Here