A00-240 · Question #38
Refer to the exhibit: Which statement is true, based on the plots above?
The correct answer is A. Approximately twice as many customers with the top ten percent of predicted probabilities are expected to have a positive predictive event.. Option A correctly describes a lift of 2.0 at the top decile, which is a standard reading from a lift chart: when you rank customers by predicted probability and target the top 10%, you capture approximately twice as many positive events as you would by randomly selecting 10% of
Question
Options
- AApproximately twice as many customers with the top ten percent of predicted probabilities are expected to have a positive predictive event.
- BApproximately ten percent of a randomly selected subset of twenty percent of the customers are expected to have a positive predictive event.
- CApproximately twenty percent of the customers with a predicted score of 3 have a positive predicted class.
- DApproximately ten percent of those customers with the top twenty percent of predicted probabilities are expected to have a positive predictive event.
How the community answered
(53 responses)- A49% (26)
- B28% (15)
- C8% (4)
- D15% (8)
Explanation
Option A correctly describes a lift of 2.0 at the top decile, which is a standard reading from a lift chart: when you rank customers by predicted probability and target the top 10%, you capture approximately twice as many positive events as you would by randomly selecting 10% of the population. This demonstrates the model's ability to concentrate positive cases at the high end of the score distribution.
Why the distractors are wrong:
- B confuses the baseline random response rate with a conditional probability - randomly selecting 20% of customers yields roughly 20% of all positive events, not 10% of that subset.
- C misreads the chart type; lift/gains charts plot cumulative population percentiles, not individual score values like "3," so no such direct reading exists.
- D inverts the logic - if only 10% of the top 20% had positive events, the model would perform at or below random chance, defeating the purpose of the model entirely.
Memory tip: Think of lift as a multiplier. A lift of 2 at the top 10% means "2× better than random." If you see a question asking about top-percentage groups and comparing to random selection, you're reading a lift chart - the answer will almost always highlight that the model outperforms random targeting.
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