Dontopedia

Metric Functions

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Metric Functions has 9 facts recorded in Dontopedia across 4 references, with 2 live disagreements.

9 facts·5 predicates·4 sources·2 in dispute

Mostly:includes(4), rdf:type(2), collectively called(1)

Maturity scale raw canonical shape-checked rule-derived certified

Inbound mentions (5)

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partOfPart of(4)

providesProvides(1)

Other facts (9)

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9 facts
PredicateValueRef
IncludesPrecision Score[3]
IncludesRecall Score[3]
IncludesF1 Score[3]
IncludesAccuracy Score[3]
Rdf:typeSklearn Functions[1]
Rdf:typeEvaluation Functions[1]
Collectively Calledsklearn metrics[2]
Common SignaturePredictions True Labels Weights[3]
Assumessorted-predictions[4]

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.

typebeam/dfbb9e1e-3e56-4d8e-b41d-1a690438b469
ex:SklearnFunctions
typebeam/dfbb9e1e-3e56-4d8e-b41d-1a690438b469
ex:Evaluation-Functions
collectivelyCalledbeam/c07ae379-ae89-4db6-8cc7-34e24961d945
sklearn metrics
commonSignaturebeam/a55e7e9c-f5ae-4d91-b7ce-cd62d5497865
ex:predictions-true-labels-weights
includesbeam/a55e7e9c-f5ae-4d91-b7ce-cd62d5497865
ex:precision_score
includesbeam/a55e7e9c-f5ae-4d91-b7ce-cd62d5497865
ex:recall_score
includesbeam/a55e7e9c-f5ae-4d91-b7ce-cd62d5497865
ex:f1_score
includesbeam/a55e7e9c-f5ae-4d91-b7ce-cd62d5497865
ex:accuracy_score
assumesbeam/7c7c4d94-1626-4327-b6b2-b57b1fc421dd
sorted-predictions

References (4)

4 references
  1. ctx:claims/beam/dfbb9e1e-3e56-4d8e-b41d-1a690438b469
  2. ctx:claims/beam/c07ae379-ae89-4db6-8cc7-34e24961d945
  3. ctx:claims/beam/a55e7e9c-f5ae-4d91-b7ce-cd62d5497865
  4. ctx:claims/beam/7c7c4d94-1626-4327-b6b2-b57b1fc421dd
    • full textbeam-chunk
      text/plain1 KBdoc:beam/7c7c4d94-1626-4327-b6b2-b57b1fc421dd
      Show excerpt
      num_queries = 1000 num_items = 10 # Generate random predictions and labels predictions = np.random.rand(num_queries, num_items) labels = np.random.randint(0, 2, size=(num_queries, num_items)) # Calculate metrics for each query ndcg_values

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