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.
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”)
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 Information-Gain for Continuous-Valued 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