You need to build a training model that accurately fits the data. The solution must minimize over fitting and minimize data leakage. Which attribute should you remove?

Note: This question is part of a series of questions that present the same Scenario. Each question I the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution while others might not have correct solution.

Start of repeated Scenario:

A Travel agency named Margie’s Travel sells airline tickets to customers in the United States.

Margie’s Travel wants you to provide insights and predictions on flight delays. The agency is considering implementing a system that will communicate to its customers as the flight departure near about possible delays due to weather conditions.

The flight data contains the following attributes:

* DepartureDate: The departure date aggregated at a per hour granularity.

* Carrier: The code assigned by the IATA and commonly used to identify a carrier.

* OriginAirportID: An identification number assigned by the USDOT to identify a unique airport (the flight’s Origin)

* DestAirportID: The departure delay in minutes.

*DepDet30: A Boolean value indicating whether the departure was delayed by 30 minutes or more ( a value of 1 indicates that the departure was delayed by 30 minutes or more)

The weather data contains the following Attributes: AirportID, ReadingDate (YYYY/MM/DD HH), SKYConditionVisibility, WeatherType, Windspeed, StationPressure, PressureChange and HourlyPrecip.

End of repeated Scenario:

You plan to predict flight delays that are 30 minutes or more.

You need to build a training model that accurately fits the data. The solution must minimize over fitting and minimize data leakage. Which attribute should you remove?
A . OriginAirportID
B . DepDel
C . DepDel30
D . Carrier
E . DestAirportID

View Answer

Answer: B