In a binary classification problem, a data scientist reports that a model achieves 'lift of 3.5 at the first decile.' What does this mean?
Select an answer to reveal the explanation.
Short Explanation and Infographic
Here's the deal — a is correct because lift at the first decile = (proportion of positives in top 10% of scored records) / (overall positive rate in population). A lift of 3.5 means the top 10% of predicted scores contains 3.5 times the proportion of actual positives compared to random selection.
Full explanation below image
Full Explanation
A is correct because lift at the first decile = (proportion of positives in top 10% of scored records) / (overall positive rate in population). A lift of 3.5 means the top 10% of predicted scores contains 3.5 times the proportion of actual positives compared to random selection. B describes a probability ratio, not the standard lift definition. C confuses lift with AUC difference. D describes cumulative capture rate (sensitivity at 10%), not lift.