Discounted cumulative gain

discounted cumulative gain

(0 Position not rated 10, 8, 9, 0, 5, 1, 4, 0, 0, 0 Sum. We use this data to compute scores or metrics, and we use these metrics for a few purposes: first, they help us understand whether one search algorithm is better than another; second, they help us understand where a search algorithm doesnt work and needs. Some familiarity with metrics that measure the quality of search results. CG, DCG, idcg, and ndcg For reading convenience, the math of interest is reproduced below (Thanks to this on-line LaTeX editor ). Figure 2 : Quepids Default Scorer Note : There are two ways to produce Figure 2 on your own. Args: r: Relevance scores (list or numpy) in rank order (first element is the first item) Returns: Average precision " r array(r)! function ratingOrDefault(posn, defVal) if return defVal; return _cRating(posn / / Build the actual list of ratings. Max_rating is the maximum value in the scorers rating scale,.g., 10 for 1-to-10 rating scale. DCG is used to emphasize highly relevant documents appearing early in the result list.

Args: r: Relevance scores (list or numpy) in rank order (first element stradivarius rabattkod is the first item) k: Number of results to consider method: If 0 then weights are.0,.0,.6309,.5,.4307. As mentioned earlier, the Quepid query scores are always scaled to the 1-to-100 range. The wrapper is essentially a pass-through to the Quepid docs and bestDocs objects, as well as the hasDocRating and docRating functions. Normalized DCG Search result lists vary in length depending on the query. Can use binary as the previous methods. Comparing a search engine's performance from one query to the next cannot be consistently achieved using DCG alone, so the cumulative gain at each position for a chosen value of should be normalized across queries. For the documents ordered by the ranking algorithm as the user provides the following relevance scores: That is: document 1 has a relevance of 3, document 2 has a relevance of 2, etc. Or, click on Custom Scorer on the top-level menu, and click on Add New : The new scorer is also initialized to the default scorers settings. In the Quepid scorers documentation for a complete API reference). Open any one of your Quepid cases, and: Either, double-click on any querys score, and click on Test with an ad-hoc scorer : The ad-hoc scorer is initialized to the default scorers settings.