Answer Happy

Exercise#1 Given a classification problem that predicts whether a borrower will default or not based on the following features: . Home Owner (either Yes or No). Marital Status (Single, Married, or Divorced). Annual Income (either High or Low). Given the following probability distribution: P(Default=“Yes”) =0.4 P(Default=“No”) =0.6 = = P(Home Owner = “Yes” | Default = “Yes”) =0.25 P(Home Owner = “Yes” | Default = “No”)=0.33 P(Marital Status = “Married” | Default =“Yes”) =0.25 P(Marital Status =“Married” | Default = “No”)=0.5 P(Annual Income = "High” | Default = “Yes”)=0.25 P(Annual Income = "High” | Default =“No”)=0.5 = = = = = Using naïve Bayesian classification, how would we classify a new instance: X= (No, Married, High)?