Example P(C)=0.01
test:
90% it is positive if you have cancer (sensitivity)
90% it is negative if you don’t have cancer (specitnity)
Bayes Rule
prior probability * test evidence -> potential probability
prior: P(c) = 0.01 = 1%
P(Positive|Cancer) = 0.9 = 90%
P(Neg|¬C)=0.9, P(positive|¬cancer) = 0.1
posterior: P(cancer|Positive) = P(Cancer)*P(Positive|C) = 0.009
P(¬cancer|Positive) = P(¬cancer)*(Positive|¬cancer) = 0.099
normalize:P(Pos)=P(cancer|Positive)+P(¬cancer|Positive)=0.108
Text Learning – Naive Bayes
Chris: love 1, deal 8, life 1
Sara: love 3, deal 2, life 3
P(Chris) = 0.5
P(Sara) = 0.5
Sara use love and life frequency.
