Using JMP :Data preprocessing. Bin the scheduled departure time $($CRS$\_$DEP$\_$TIME$)$ into $8$ bins. This will avoid treating the departure time as a continuous predictor, because it is reasonable that delays are related to rush$-$hour times. $($Note that these data are not stored in JMP with a time format, so you'll need to explore the best way to bin this data $-$two options are $(1)$ via the formula editor and $(2)$ using the Make Binning Formula column utility.$)$ Partition the data into training and validation sets.

Fit a classification tree to the flight delay variable using all the relevant predictors $($use the binned version of the departure time$)$ and the validation column. Do not include DEP$\_$TIME $($actual departure time$)$ in the model because it is unknown at the time of prediction $($unless we are doing our predicting of delays after the plane takes off, which is unlikely$).$

How many splits are in the final model?

How many variables are involved in the splits?

Which variables contribute the most to the model?

Which variables were not involved in any of the splits?

Express the resulting tree as a set of rules.

If you needed to fly between DCA and EWR on a Monday at $7$ AM$,$ would you be able to use this tree to predict whether the flight will be delayed? What other information would you need? Is this information available in practice? What information is redundant?