Metrics explained: nDCG@K
📰 Medium · LLM
Learn to use nDCG@K to evaluate the ranking quality of your retriever model, which is crucial for information retrieval and search systems
Action Steps
- Calculate the relevance of each result using a scoring function
- Rank the results based on their relevance scores
- Compute the discounted cumulative gain (DCG) for the top K results
- Calculate the ideal DCG (IDCG) for the top K results
- Compute the normalized DCG (nDCG) by dividing DCG by IDCG
Who Needs to Know This
Data scientists and machine learning engineers benefit from understanding nDCG@K to improve their model's performance and relevance, while product managers can use it to evaluate the effectiveness of their search systems
Key Insight
💡 nDCG@K measures the ranking quality of a retriever model by giving higher credit to highly relevant results that appear early in the ranking
Share This
📈 Improve your search model's ranking quality with nDCG@K!
Key Takeaways
Learn to use nDCG@K to evaluate the ranking quality of your retriever model, which is crucial for information retrieval and search systems
DeepCamp AI