AI-900 Question 112
Single answerYou are tasked with building a machine learning model to predict the price of houses based on a dataset. The dataset contains the following columns: 'SquareFootage', 'NumberOfBedrooms', 'NumberOfBathrooms', 'Neighborhood', and 'Price'. Which column should be identified as the label in the dataset?
- A
SquareFootage
- B
NumberOfBedrooms
- C
NumberOfBathrooms
- D
Neighborhood
- E
Price
Show answer and explanation
Correct answer: E
Explanation
In supervised machine learning, the label is the target output that the model is trained to predict. Features are the input variables used to make this prediction. In this scenario, 'Price' is the label because it represents the output value we want the model to predict, while the other columns ('SquareFootage', 'NumberOfBedrooms', 'NumberOfBathrooms', and 'Neighborhood') are features that provide input data to the model.
- A. Incorrect.
'SquareFootage' is a feature that represents a property characteristic but is not the value being predicted.
- B. Incorrect.
'NumberOfBedrooms' is a feature that represents a property characteristic but is not the value being predicted.
- C. Incorrect.
'NumberOfBathrooms' is a feature that represents a property characteristic but is not the value being predicted.
- D. Incorrect.
'Neighborhood' is a feature that represents the location of a property but is not the value being predicted.
- E. Correct.
'Price' is the label because it is the target variable that the machine learning model is being trained to predict.