DRAG DROP
Note: This question is part of a series of questions that use the same scenario. For your convenience, the
scenario is repeated in each question. Each question presents a different goal and answer choices, but the text
of the scenario is exactly the same in each question in this series.
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 nears 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
OriginAitportID: An identification number assigned by the USDOT to identify a unique airport (the flight’s
origin)
DestAirportID: An identification number assigned by the USDOT to identify a unique airport (the flight’s
destination)
DepDel: The departure delay in minutes
DepDel30: 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.
You need to remove the bias and to identify the columns in the input dataset that have the greatest predictive
power.
Which module should you use for each requirement? To answer, drag the appropriate modules to the correctrequirements. Each module may be used once, more than once, or not at all. You may need to drag the split
bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Select and Place:
Answer: 
Explanation:
https://gallery.cortanaintelligence.com/Experiment/Binary-Classification-Flight-delay-prediction-3
https://msdn.microsoft.com/library/azure/038d91b6-c2f2-42a1-9215-1f2c20ed1b40
Filter Based Feature Selection module to identify columns that have greatest predictive power.
1
0
Filter Based Feature Selection
・Used to identify the columns in your input dataset that have the greatest predictive power.
0
0
What is the correct answer?
0
0
Cross-validation (using Cross-validate model) to remove bias
and
using Filter Based Feature Selection to identify the columns in your input dataset that have the greatest predictive power.
3
0
Besides, part of that new 45Q 70-774 dumps are available here:
https://drive.google.com/open?id=1kasCFEWbbVbNGXhtQ5AfwMOgYaKryNdH
Best Regards!
0
0
How about evaluate model for the first one? Not quite sure but I believe this might be also correct?
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/evaluate-model
” looking at the pattern of the residuals (the difference between any one predicted point and its corresponding actual value) can tell you a lot about potential bias in the model.”
0
0