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   The ranking evaluation API provides a convenient way to use this information in a ranking evaluation request to calculate different search evaluation metrics. This gives a first estimation of your overall search quality and give you a measurement to optimize against when fine-tuning various aspect of the query generation in your application. 
   
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   === Ranking evaluation request structure
   
   In its most basic form, a request to the `_rank_eval` endpoint has two sections:
  
positive.code: "relevant". Defaults to `1`.
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  ==== Discounted cumulative gain (DCG)
  
  In contrast to the two metrics above, https://en.wikipedia.org/wiki/Discounted_cumulative_gain[discounted cumulative gain] takes both, the rank and the rating of the search results, into account.
  
negative.code: <3> a reference to a previously defined temlate
  <4> the parameters to use to fill the template
  
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  === Available evaluation metrics
  
  The `metric` section determines which of the available evaluation metrics is going to be used.
  Currently, the following metrics are supported:
  
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  ==== Precision at K (P@k)
  
  This metric measures the number of relevant results in the top k search results. Its a form of the well known https://en.wikipedia.org/wiki/Information_retrieval#Precision[Precision] metric that only looks at the top k documents. It is the fraction of relevant documents in those first k
  