AI engineering glossary · Evaluation metrics
What is DCG (discounted cumulative gain)?
Discounted cumulative gain (also called DCG): A ranking score that sums the relevance grade of each result divided by the log of its position: DCG = sum of rel_i / log2(i + 1). Highly relevant items near the top add the most, and gains shrink further down the list. It handles graded, not just yes-or-no, relevance.
Discounted cumulative gain: an example
Relevance grades 3, 2, 0 at ranks 1, 2, 3 give DCG = 3/1 + 2/1.585 + 0 = about 4.26.
Related terms
- Normalised discounted cumulative gain
- Mean average precision
- Expected reciprocal rank
- Rank-biased precision
More Evaluation metrics terms
- Dunn index
- Error analysis
- Exact match
- Expected calibration error
- Expected reciprocal rank
- Explained variance score
- F-beta score
- F1 score
Where you learn it
Discounted cumulative gain is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.