Predicting Delayed Flights (Boosting). The file FlightDelays.csv contains information

on all commercial flights departing the Washington, DC area and arriving at New

York during January 2004. For each flight there is information on the departure and

arrival airports, the distance of the route, the scheduled time and date of the flight, and

so on. The variable that we are trying to predict is whether or not a flight is delayed.

A delay is defined as an arrival that is at least 15 minutes later than scheduled

Data Preprocessing. Transform variable day of week info a categorical variable. Bin

the scheduled departure time into eight bins (in R use function cut()). Partition the

data into training and validation sets.

Run a boosted classification tree for delay. Leave the default number of weak

learners, and select resampling. Set maximum levels to display at 6, and minimum

number of records in a terminal node to 1.

a. Compared with the single tree, how does the boosted tree behave in terms of overall

accuracy?

b. Compared with the single tree, how does the boosted tree behave in terms of

accuracy in identifying delayed flights?

c. Explain why this model might have the best performance over the other models

you fit.