Explore how trip distance plays a crucial role in predicting taxi fare. Discover why trip distance is a key feature in training models for accurate fare predictions.
Table of Contents
Question
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?
A. the number of taxi journeys in the dataset
B. the trip distance of individual taxi journeys
C. the fare of individual taxi journeys
D. the trip ID of individual taxi journeys
Answer
B. the trip distance of individual taxi journeys
Explanation
For predicting the fare of a taxi journey, the suitable feature from the dataset would be option B: the trip distance of individual taxi journeys. Trip distance is a relevant factor influencing the fare of a taxi ride, making it a valuable feature for training a model to predict fare amounts based on historical data.
The label is the column you want to predict. The identified Features are the inputs you give the model to predict the Label.
Example: 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.
Microsoft Azure AI Fundamentals AI-900 certification exam practice question and answer (Q&A) dump with detail explanation and reference available free, helpful to pass the Microsoft Azure AI Fundamentals AI-900 exam and earn Microsoft Azure AI Fundamentals AI-900 certification.
