Use explanation plz; don't copy other answers, they are wrong.

Assume we have the following data set that presents whether or not a car is stolen depending on the color, type and the origin of the car. Each instance (example) is represented by the four attributes. - Color: a value of { Red, Yellow }. - Type: a value of { Sports, SUV }. - Origin: a value of { Domestic, Imported }. - Stolen: the classification attribute (i.e., Yes means the car is stolen; No means the car is not stolen). We want to classify a Red, Domestic, and SUV. Note there is no example of a Red Domestic SUV in our data set. For the given test record: X= (Color = Red, Type =SUV, Origin = Domestic ), what is the "Stolen" prediction? Use Laplace Smoothing using m-estimate to train the Niave Bayes classifier where ( m= number of distinct word) Assume we have the following data set that presents whether or not a car is stolen depending on the color, type and the origin of the car. Each instance (example) is represented by the four attributes. - Color: a value of { Red, Yellow }. - Type: a value of { Sports, SUV }. - Origin: a value of { Domestic, Imported }. - Stolen: the classification attribute (i.e., Yes means the car is stolen; No means the car is not stolen). We want to classify a Red, Domestic, and SUV. Note there is no example of a Red Domestic SUV in our data set. For the given test record: X= (Color = Red, Type =SUV, Origin = Domestic ), what is the "Stolen" prediction? Use Laplace Smoothing using m-estimate to train the Niave Bayes classifier where ( m= number of distinct word)