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Consider the traffic accident data set shown in the following table.

Let Crash Severity be the class label. Show the key steps for the following tasks.

(a) Using information gain as the attribute selection measure, construct the first level of the decision tree.

(b) If gain ratio is used as the attribute selection measure, will the first level of the decision tree be different from above?

(c) Given a traffic accident with the values Good, Sober, None, and Yes for the attributes Weather Condition, Drivers Condition, Traffic Violation, and Seat Belt, respectively, how would a nave Bayesian classifier determine whether Crash Severity would be Minor or Major?

Consider the traffic accident data set shown in the following table. SeatCrash Weather Condition Bad Good Bad Driver's Conditioin Sober Sober Sober Traffic Violation None BeltSeverity Yes Minor Disobey stop sign Yes Minor Yes Minor GoodAlcohol-impaired Exceed speed limitNoMajor Disobey traffic signal No Major GoodAlcohol-impaired Disobey stop signYes Minor GoodAlcohol-impaired Exceed speed limit Yes Major YesMajor Disobey traffic signalYes Major No Major Disobey traffic signal No Major Exceed speed limitYes Major Disobey stop sign Bad Sober Alcohol-impaired None Bad Good Good Alcohol-impaired Bad Good Sober None Sober Sober Let Crash Severity be the class label. Show the key steps for the following tasks. (a) Using information gain as the attribute selection measure. construct the first level of the decision tree (b) gain ratio is used as the attribute selection measure, will the first level of the decision tree be dfferent from above? (c) Given a traffic accident with the values "Good. Sober. "None" and "Yes for the attributes Weather Condition, Driver's Condtion, Traffic Violation, and Seat Belt, respectively, howr would a naive Bayesian classifier determine whether Crash Sevenity would be Minor or Major? Consider the traffic accident data set shown in the following table. SeatCrash Weather Condition Bad Good Bad Driver's Conditioin Sober Sober Sober Traffic Violation None BeltSeverity Yes Minor Disobey stop sign Yes Minor Yes Minor GoodAlcohol-impaired Exceed speed limitNoMajor Disobey traffic signal No Major GoodAlcohol-impaired Disobey stop signYes Minor GoodAlcohol-impaired Exceed speed limit Yes Major YesMajor Disobey traffic signalYes Major No Major Disobey traffic signal No Major Exceed speed limitYes Major Disobey stop sign Bad Sober Alcohol-impaired None Bad Good Good Alcohol-impaired Bad Good Sober None Sober Sober Let Crash Severity be the class label. Show the key steps for the following tasks. (a) Using information gain as the attribute selection measure. construct the first level of the decision tree (b) gain ratio is used as the attribute selection measure, will the first level of the decision tree be dfferent from above? (c) Given a traffic accident with the values "Good. Sober. "None" and "Yes for the attributes Weather Condition, Driver's Condtion, Traffic Violation, and Seat Belt, respectively, howr would a naive Bayesian classifier determine whether Crash Sevenity would be Minor or Major