IV . Compute manually the naive Bayes conditional probability of all injury giveIl WEATHER R = 1 and TRAF CON R = 1 . V . Run a naive Bayes classifier on the 12 records and two predictors using R . Check the model output to obtain probabilities and classifications for all 12 records . Compare this to the exact Bayes classification . Are the resulting classifications equivalent ? Is the razzking ( = ordering ) of observations equivalent ?" Let us now return to the entire dataset . Partition the data into training ( 60% ) and validation ( 40% ) 1 . Assuming that no information or initial reports about the accident itself are* available at the time of prediction (only location characteristics , Weather" conditions , etc. ), Which predictors call we include in the analysis ? 'IIse the