What are the different ways to handle False Positives and False Negatives when computing NDCG on a Search System results?
Context:
I am using NDCG (Normalized Discounted Cumulative Gain) to evaluate a Semantic Search system on a ground truth dataset containing relevance scores. I would like to use sklearn’s ndcg_score() for this.