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 have an untrained Azure Machine Learning model that you plan to train to predict flight delays.
You need to assess the variability of the dataset and the reliability of the predictions from the model.
Which module should you use?
A.
Cross-Validate Model
B.
Evaluate Model
C.
Tune Model Hyperparameters
D.
Train Model
E.
Score Model
Explanation:
https://msdn.microsoft.com/en-us/library/azure/dn905852.aspx
is it not D.Train Model?
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No.
Cross-Validate Model
Used to assess the variability of the dataset and the reliability of the predictions.
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Train Model
module in Azure Machine Learning Studio to train a classification or regression model. Training takes place after you have defined a model and set its parameters, and requires tagged data. You can also use Train Model to retrain an existing model with new data.
Cross-Validate Model
module in Azure Machine Learning Studio. Cross-validation is an important technique often used in machine learning to assess both the variability of a dataset and the reliability of any model trained using that data.
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