Dontopedia
Explore

Precision at K Metric

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-08.)

Precision at K Metric has 4 facts recorded in Dontopedia across 2 references, with 1 live disagreement.

4 facts·3 predicates·2 sources·1 in dispute
Maturity scale raw canonical shape-checked rule-derived certified

Rdf:typein disputerdf:type

Imported FromimportedFrom

Defined Asdefined-as

Inbound mentions (5)

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.

doesNotProvideImplementationForDoes Not Provide Implementation for(1)

hasMemberHas Member(1)

providesDefinitionForProvides Definition for(1)

referencesReferences(1)

suggestsSuggests(1)

Timeline

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.

4 undated
—
defined-asbeam/166e449f-f01f-4d52-b7b4-50e375d9caff
ex:precision-of-top-k-retrieved-documents
—
importedFrombeam/0101eba2-9f85-41c1-ac05-d4c55e85d3fc
ex:scikit-learn-metrics
—
typebeam/0101eba2-9f85-41c1-ac05-d4c55e85d3fc
ex:EvaluationMetric
—
typebeam/166e449f-f01f-4d52-b7b4-50e375d9caff
ex:TopKMetric

References (2)

2 references
  1. [1]beam-chunk2 facts
    customctx:claims/beam/166e449f-f01f-4d52-b7b4-50e375d9caff
    • full textbeam-chunk
      text/plain1 KBdoc:beam/166e449f-f01f-4d52-b7b4-50e375d9caff
      Show excerpt
      print(f"Precision: {precision}, Recall: {recall}, F1 Score: {f1_score}") ``` Can you help me fill in the evaluation logic and suggest some additional metrics I can use? ->-> 1,1 [Turn 6081] Assistant: Certainly! Evaluating the performance …
  2. [2]beam-chunk2 facts
    customctx:claims/beam/0101eba2-9f85-41c1-ac05-d4c55e85d3fc
    • full textbeam-chunk
      text/plain1 KBdoc:beam/0101eba2-9f85-41c1-ac05-d4c55e85d3fc
      Show excerpt
      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…

See also

Keep researching

Missing something or suspicious of what's here? Kick off a research session — a Claude agent will investigate, cite its sources, and file new facts into a dedicated context you can review before accepting into the shared view.

↑