NDCG@k: Graded Relevance and the Ideal Ranking
This lesson is part of the course
In this lesson
You score one ranked result list with graded relevance: discounted gain per rank with LOG(rank + 1, 2), the ideal ranking with LARGE, then DCG@k, IDCG@k and NDCG@k with k in a cell.
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Lessons in this course
- Search Quality: Precision@k, Recall@k & Hit Rate Locked
- Mean Reciprocal Rank: How Soon Is the First Good Answer? Locked
- NDCG@k: Graded Relevance and the Ideal Ranking Locked
- Average Precision & MAP: Rewarding Good Order Locked
- Thresholds, the ROC Curve & AUC by Trapezoids Locked
- Calibration: Does 90% Confident Mean 90% Correct? Locked
- LLM-as-a-Judge: Percent Agreement & Cohen's Kappa Locked
- A/B Testing Two Models: Win Rate, Ties & a Confidence Interval Locked