Exams / AIF-C01 / Use cases and ML techniques / Question 2
AIF-C01 practice question: Use cases and ML techniques (2 of 5)
Fundamentals of AI and ML A free question from our original AIF-C01 practice exam. Try it, then open the answer to see why each option is right or wrong.
An estate agency wants a model that predicts the likely sale price of a house from its size, location and number of bedrooms. It has records of past sales with their final prices. Which type of ML problem is this?
Choose one answer.
- AA. Regression, because the output is a continuous numeric value
- BB. Binary classification, because each house either sells or not
- CC. Clustering, because similar houses can be grouped together
- DD. Anomaly detection, because some prices may be unusual
Show the answer and explanation
Answer: A
- A. Right: Correct. Predicting a number such as a price from labelled past examples is supervised regression.
- B. Wrong: The agency wants the price, not a yes or no outcome, so classification does not match the goal.
- C. Wrong: Clustering groups unlabelled records. It does not predict a specific value for each house.
- D. Wrong: Anomaly detection flags rare or unusual records. It does not estimate the price of a new listing.
Explanation
Regression predicts a continuous numeric value (such as a price or temperature), while classification predicts a category. Because the agency has past sales with known prices, this is supervised regression.
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