You have a dataset that contains information about taxi journeys that occurred during a given period. You need to train a model to predict the fare of a taxi journey. What should you use as a feature?

Question

Options

• Athe number of taxi journeys in the dataset
• Bthe trip distance of individual taxi journeys
• Cthe fare of individual taxi journeys
• Dthe trip ID of individual taxi journeys

• A
  3% (1)
• B
  84% (31)
• C
  8% (3)
• D
  5% (2)

Explanation

The label is the column you want to predict. The identified Featuresare the inputs you give the model to predict the Label. The provided data set contains the following columns: vendor_id: The ID of the taxi vendor is a feature. rate_code: The rate type of the taxi trip is a feature. passenger_count: The number of passengers on the trip is a feature. trip_time_in_secs: The amount of time the trip took. You want to predict the fare of the trip before the trip is completed. At that moment, you don't know how long the trip would take. Thus, the trip time is not a feature and you'll exclude this column from the model. trip_distance: The distance of the trip is a feature. payment_type: The payment method (cash or credit card) is a feature. fare_amount: The total taxi fare paid is the label. https://docs.microsoft.com/en-us/dotnet/machine-learning/tutorials/predict-prices

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