18. Given a car theft training data set which has 3 attributes/features and a class. The 3 attributes are Color, Type and Origin. The class is Stolen. Example No. Color T Orgin Stolen? Sports DomesticYes Red Sports Domestic No Red Sports Domestic Yes 3 Yellow Sports Domesti Yellow Sports ImportedYes Yellow SUV Imported No Yellow SUV ImportedYes YellowSUV Domestic No Red SUV Imported No Red Sports Imported| Yes 9 10 a) Suppose you want to build a decision tree, given the three attributes: Color, Type and Origin, what would be your choice of first root node for the decision tree you are going to build. Show your result by calculating the information gain of each attribute b) The probabilities required for classification are listed as follows. P(Stolen-Yes)-%, P(Red | Stolen-Yes)-3/5 P(Yellow Stolen-Yes)-2/5 P(Sports | Stolen-Yes) 3/5 P(SUV Stolen-Yes) 1/5 P(Domestic | Stolen-Yes)- 2/5 P(Domestic | Stolen -No)- 3/5 P(Imported | Stolen-Yes)- 3/5P(Imported | Stolen No)- 2/5 P(Stolen-No) 2 P(Red | Stolen No) 2/5 P(Yellow Stolen-No) 3/5 P(Sports | Stolen -No) 2/5 P(SUV Stolen-No) 3/5 We want to classify a {Red, SUV, Domestic), whether the car will be stolen or not. Justify your answer using the nave Bayesian classifier