Just like the PlayTennis dataset, the features are binary$-$valued but note that some features have missing values for some rows $($instances$).$ You need to decide how you will handle them. There are three possibilities here: i$)$ discard instances that have missing feature values, ii$)$ treat $$missing$$ as if it is a value $($and thus a binary feature becomes a ternary, or three$-$valued, feature$),$ iii$)$ impute missing values $($i$.$e$.,$ for each feature, replace missing values with the most common value for that feature$),$ so that they are no longer missing or unknown. If you read the $.$notes$$ file, it explains why some values are missing and what they mean. $$ Implement a decision tree and Na$$ve Bayes classifier for classification, with each of the above three ways of dealing with missing values. So you are experimenting with $6$ scenarios. $$ Perform $5-$fold cross validation and report precision, recall, and F$1-$scores for each of the $6$ scenarios. $2.(20+20=40$ points$)$ For what type of dataset would you choose decision trees as a classifier over Naive Bayes? Vice versa?