Precision@k

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Precision@k is proportion of relevant documents among the top k retrieved documents.

28 facts·15 predicates·9 sources·3 in dispute

Mostly:rdf:type(8), description(2), measures(1)

Inbound mentions (24)

Other subjects in dontopedia point AT this entity as a value. These are inverse relationships — e.g. "X motherOf this subject" — and answer questions the forward facts can't. Grouped by predicate.

`contains`Contains(2)

Additional Metrics Sectionex:additional-metrics-section
Step 1ex:step-1

`includes`Includes(2)

Evaluation Metricsex:evaluation-metrics
Evaluation Metricsex:evaluation-metrics

`characterizes`Characterizes(1)

Retrieval Focusex:retrieval-focus

`describes`Describes(1)

Precision Recall Explanationex:precision-recall-explanation

`hasExample`Has Example(1)

Evaluation Metricex:evaluation-metric

`hasMember`Has Member(1)

Metric Listex:metric-list

`imports`Imports(1)

Evaluation Sectionex:evaluation-section

`improvedBy`Improved by(1)

Relevant Metricsex:relevant-metrics

`includesMetrics`Includes Metrics(1)

Bm25 Evaluation Contextex:bm25-evaluation-context

`inverseOf`Inverse of(1)

Recall at Kex:recall-at-k

`measuredBy`Measured by(1)

Retrieval Performanceex:retrieval-performance

`mentioned`Mentioned(1)

Assistantex:assistant

`monitoredBy`Monitored by(1)

Trainingex:training

`optimizedFor`Optimized for(1)

Alphaex:alpha

`provides`Provides(1)

Sklearnex:sklearn

`relatedMetric`Related Metric(1)

Recall at Kex:recall-at-k

`relatedTo`Related to(1)

Mean Precision at Kex:mean-precision-at-k

`relates`Relates(1)

Metric Relationshipsex:metric-relationships

`specificVariant`Specific Variant(1)

Map at 10ex:map-at-10

`suggestsAdding`Suggests Adding(1)

Evaluation Extensionex:evaluation-extension

`usedIn`Used in(1)

Top K Conceptex:top-k-concept

`uses`Uses(1)

Evaluation Metricex:evaluation-metric

Other facts (23)

The long tail: predicates that appear too rarely to warrant their own section. Filter or scroll to find a specific one. Each row links to its source.

23 facts

Predicate	Value	Ref
Rdf:type	Evaluation Metric	[1]
Rdf:type	Metric	[2]
Rdf:type	Evaluation Metric	[3]
Rdf:type	Evaluation Metric	[4]
Rdf:type	Metric	[5]
Rdf:type	Ranking Metric	[7]
Rdf:type	Evaluation Metric	[8]
Rdf:type	Information Retrieval Metric	[9]
Description	proportion of relevant documents among the top k retrieved documents	[1]
Description	Focuses on top-k retrieved documents	[3]
Measures	Proportion of Relevant Documents	[1]
Applies to	Top K Retrieved Documents	[1]
Described As	Precision of the top-k retrieved documents	[2]
Related Metric	Recall at K	[2]
Inverse of	Recall at K	[2]
Abbreviation	P@k	[4]
From Library	Scikit Learn	[5]
Is Evaluation Metric	Metric	[6]
Imported From	Sklearn.metrics	[6]
Is Example of	Ranking Metrics	[7]
Is Metric for	Ranking Evaluation	[7]
Monitored During	Training	[8]
Purpose	Ensuring Model Improvement on Relevant Metrics	[8]

Timeline axis is valid_time — when each source says the fact was true in the world, not when Dontopedia learned about it. Retracted rows are kept for provenance; coloured stripes indicate the context kind.

28 undated

—

typebeam/3da08fad-f16a-47c2-9861-9ad0d160b9a4

ex:evaluation-metric

—

measuresbeam/3da08fad-f16a-47c2-9861-9ad0d160b9a4

ex:proportion-of-relevant-documents

—

descriptionbeam/3da08fad-f16a-47c2-9861-9ad0d160b9a4

proportion of relevant documents among the top k retrieved documents

—

labelbeam/3da08fad-f16a-47c2-9861-9ad0d160b9a4

Precision@k

—

appliesTobeam/3da08fad-f16a-47c2-9861-9ad0d160b9a4

ex:top-k-retrieved-documents

—

typebeam/23c0eddb-0929-4239-8d55-13531af3e8f5

ex:Metric

—

labelbeam/23c0eddb-0929-4239-8d55-13531af3e8f5

Precision@k

—

describedAsbeam/23c0eddb-0929-4239-8d55-13531af3e8f5

Precision of the top-k retrieved documents

—

relatedMetricbeam/23c0eddb-0929-4239-8d55-13531af3e8f5

ex:recall-at-k

—

inverseOfbeam/23c0eddb-0929-4239-8d55-13531af3e8f5

ex:recall-at-k

—

typebeam/84eee47d-7fea-4e98-8d74-9eb5dc8c1b85

ex:EvaluationMetric

—

labelbeam/84eee47d-7fea-4e98-8d74-9eb5dc8c1b85

Precision@k

—

descriptionbeam/84eee47d-7fea-4e98-8d74-9eb5dc8c1b85

Focuses on top-k retrieved documents

—

typebeam/f7999e0a-925c-4a2e-afc4-b5e2483ddb0a

ex:evaluation-metric

—

abbreviationbeam/f7999e0a-925c-4a2e-afc4-b5e2483ddb0a

P@k

—

typebeam/0101eba2-9f85-41c1-ac05-d4c55e85d3fc

ex:Metric

—

fromLibrarybeam/0101eba2-9f85-41c1-ac05-d4c55e85d3fc

ex:scikit-learn

—

labelbeam/0101eba2-9f85-41c1-ac05-d4c55e85d3fc

precision at k

—

isEvaluationMetricbeam/cc7e2701-5558-4a53-b31f-07382bf903bd

ex:metric

—

importedFrombeam/cc7e2701-5558-4a53-b31f-07382bf903bd

ex:sklearn.metrics

—

typebeam/685289a8-df46-4c0b-b3eb-bb8cac2dcb73

ex:RankingMetric

—

isExampleOfbeam/685289a8-df46-4c0b-b3eb-bb8cac2dcb73

ex:ranking-metrics

—

isMetricForbeam/685289a8-df46-4c0b-b3eb-bb8cac2dcb73

ex:ranking-evaluation

—

typebeam/864c2d75-2f47-4635-8d2e-4fe6efdd0312

ex:EvaluationMetric

—

labelbeam/864c2d75-2f47-4635-8d2e-4fe6efdd0312

Precision@k

—

monitoredDuringbeam/864c2d75-2f47-4635-8d2e-4fe6efdd0312

ex:training

—

purposebeam/864c2d75-2f47-4635-8d2e-4fe6efdd0312

ex:ensuring-model-improvement-on-relevant-metrics

—

typebeam/a18f983c-7bcb-4682-a34d-8c0445e82651

ex:InformationRetrievalMetric

References (9)

9 references

[1]3da08fad F16a 47c2 9861 9ad0d160b9a45 facts
ctx:claims/beam/3da08fad-f16a-47c2-9861-9ad0d160b9a4
- full textbeam-chunk
  text/plain1 KBdoc:beam/3da08fad-f16a-47c2-9861-9ad0d160b9a4
  Show excerpt
  [Turn 6077] Assistant: Fine-tuning the `alpha` value to balance sparse and dense retrieval is crucial for optimizing the performance of your hybrid retrieval system. Here are some steps and methods you can use to find the optimal `alpha` va…
[2]23c0eddb 0929 4239 8d55 13531af3e8f55 facts
ctx:claims/beam/23c0eddb-0929-4239-8d55-13531af3e8f5
- full textbeam-chunk
  text/plain1 KBdoc:beam/23c0eddb-0929-4239-8d55-13531af3e8f5
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  - **Average Precision (AP)**: Measure of precision at each relevant document. 4. **Mean Scores**: Calculate the mean of each metric across all queries. ### Additional Metrics 1. **Precision@k**: Precision of the top-k retrieved documen…
[3]84eee47d 7fea 4e98 8d74 9eb5dc8c1b853 facts
ctx:claims/beam/84eee47d-7fea-4e98-8d74-9eb5dc8c1b85
- full textbeam-chunk
  text/plain1 KBdoc:beam/84eee47d-7fea-4e98-8d74-9eb5dc8c1b85
  Show excerpt
  print(f"Mean Precision: {mean_precision}, Mean Recall: {mean_recall}, Mean F1 Score: {mean_f1}, Mean AP: {mean_ap}, Mean Precision@{k}: {mean_precision_at_k}, Mean Recall@{k}: {mean_recall_at_k}") ``` ### Explanation 1. **Precision@k and …
[4]F7999e0a 925c 4a2e Afc4 B5e2483ddb0a2 facts
ctx:claims/beam/f7999e0a-925c-4a2e-afc4-b5e2483ddb0a
- full textbeam-chunk
  text/plain1 KBdoc:beam/f7999e0a-925c-4a2e-afc4-b5e2483ddb0a
  Show excerpt
  3. **Evaluation Metrics**: Use appropriate evaluation metrics to measure the relevance lift. Common metrics include Precision@k, Recall, and Mean Average Precision (MAP). 4. **Post-processing**: Consider post-processing steps such as re-ra…
[5]0101eba2 9f85 41c1 Ac05 D4c55e85d3fc3 facts
ctx:claims/beam/0101eba2-9f85-41c1-ac05-d4c55e85d3fc
- full textbeam-chunk
  text/plain1 KBdoc:beam/0101eba2-9f85-41c1-ac05-d4c55e85d3fc
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  if max_score == min_score: return np.zeros_like(scores) return (scores - min_score) / (max_score - min_score) def hybrid_ranking(sparse_scores, dense_scores, alpha=0.6): # Normalize scores to ensure they are on the same…
[6]Cc7e2701 5558 4a53 B31f 07382bf903bd2 facts
ctx:claims/beam/cc7e2701-5558-4a53-b31f-07382bf903bd
- full textbeam-chunk
  text/plain1 KBdoc:beam/cc7e2701-5558-4a53-b31f-07382bf903bd
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  dense_scores = np.array([0.7, 0.3, 0.1]) # Normalize and compute hybrid scores hybrid_scores = hybrid_ranking(sparse_scores, dense_scores) print(hybrid_scores) # Optionally, sort documents based on hybrid scores sorted_indices = np.argsor…
[7]685289a8 Df46 4c0b B3eb Bb8cac2dcb733 facts
ctx:claims/beam/685289a8-df46-4c0b-b3eb-bb8cac2dcb73
- full textbeam-chunk
  text/plain1 KBdoc:beam/685289a8-df46-4c0b-b3eb-bb8cac2dcb73
  Show excerpt
  [Turn 6423] Assistant: Certainly! Addressing ranking issues in your RAG system and achieving 95% detection for 25,000 hybrid queries requires a systematic debugging strategy. Here are the steps you can follow to identify and resolve ranking…
[8]864c2d75 2f47 4635 8d2e 4fe6efdd03124 facts
ctx:claims/beam/864c2d75-2f47-4635-8d2e-4fe6efdd0312
- full textbeam-chunk
  text/plain1 KBdoc:beam/864c2d75-2f47-4635-8d2e-4fe6efdd0312
  Show excerpt
  - **Margin**: Adjust the margin in contrastive loss functions to penalize incorrect predictions more heavily. ### 5. **Evaluation Metrics** - **Precision@k**: Monitor Precision@k metrics during training to ensure the model is improvi…
[9]A18f983c 7bcb 4682 A34d 8c0445e826511 fact
ctx:claims/beam/a18f983c-7bcb-4682-a34d-8c0445e82651
- full textbeam-chunk
  text/plain1 KBdoc:beam/a18f983c-7bcb-4682-a34d-8c0445e82651
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  - **Joblib**: The `joblib` library is used for parallel computing in Python. It provides a simple interface to parallelize tasks and manage the parallel execution of functions. By using this parallel implementation, you can significantly r…

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Precision@k

Inbound mentions (24)

containsContains(2)

includesIncludes(2)

characterizesCharacterizes(1)

describesDescribes(1)

hasExampleHas Example(1)

hasMemberHas Member(1)

importsImports(1)

improvedByImproved by(1)

includesMetricsIncludes Metrics(1)

inverseOfInverse of(1)

measuredByMeasured by(1)

mentionedMentioned(1)

monitoredByMonitored by(1)

optimizedForOptimized for(1)

providesProvides(1)

relatedMetricRelated Metric(1)

relatedToRelated to(1)

relatesRelates(1)

specificVariantSpecific Variant(1)

suggestsAddingSuggests Adding(1)

usedInUsed in(1)

usesUses(1)

Other facts (23)

Timeline

References (9)

See also

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`contains`Contains(2)

`includes`Includes(2)

`characterizes`Characterizes(1)

`describes`Describes(1)

`hasExample`Has Example(1)

`hasMember`Has Member(1)

`imports`Imports(1)

`improvedBy`Improved by(1)

`includesMetrics`Includes Metrics(1)

`inverseOf`Inverse of(1)

`measuredBy`Measured by(1)

`mentioned`Mentioned(1)

`monitoredBy`Monitored by(1)

`optimizedFor`Optimized for(1)

`provides`Provides(1)

`relatedMetric`Related Metric(1)

`relatedTo`Related to(1)

`relates`Relates(1)

`specificVariant`Specific Variant(1)

`suggestsAdding`Suggests Adding(1)

`usedIn`Used in(1)

`uses`Uses(1)