Dontopedia

Accuracy Percentage

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-10.)

Accuracy Percentage has 9 facts recorded in Dontopedia across 3 references, with 2 live disagreements.

9 facts·7 predicates·3 sources·2 in dispute

Mostly:rdf:type(2), computed from(2), uses formula(1)

Maturity scale raw canonical shape-checked rule-derived certified

Inbound mentions (4)

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.

includesIncludes(2)

calculatesCalculates(1)

containsContains(1)

Other facts (9)

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.

9 facts
PredicateValueRef
Rdf:typePercentage[1]
Rdf:typeMetric[3]
Computed FromTotal Time[1]
Computed FromExpected Time[1]
Uses FormulaAccuracy Formula[1]
Display FormatTwo Decimal Places[1]
PurposePerformance Evaluation[1]
Calculationaccuracy * 100[2]
Is Monitored ViaKibana[3]

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/7da9ea7b-c0ac-49fd-b423-5ee8dee6084a
ex:Percentage
computedFrombeam/7da9ea7b-c0ac-49fd-b423-5ee8dee6084a
ex:total-time
computedFrombeam/7da9ea7b-c0ac-49fd-b423-5ee8dee6084a
ex:expected-time
usesFormulabeam/7da9ea7b-c0ac-49fd-b423-5ee8dee6084a
ex:accuracy-formula
displayFormatbeam/7da9ea7b-c0ac-49fd-b423-5ee8dee6084a
ex:two-decimal-places
purposebeam/7da9ea7b-c0ac-49fd-b423-5ee8dee6084a
ex:performance-evaluation
calculationbeam/f6d6e5e8-2e81-4b5b-8ad1-a93a9616694c
accuracy * 100
typebeam/1fc14f37-f4dc-462b-8ced-d7ac65395d13
ex:Metric
isMonitoredViabeam/1fc14f37-f4dc-462b-8ced-d7ac65395d13
ex:kibana

References (3)

3 references
  1. ctx:claims/beam/7da9ea7b-c0ac-49fd-b423-5ee8dee6084a
    • full textbeam-chunk
      text/plain1 KBdoc:beam/7da9ea7b-c0ac-49fd-b423-5ee8dee6084a
      Show excerpt
      documents = [f"document_{i}" for i in range(18000)] start_time = datetime.now() ingest_documents(documents) end_time = datetime.now() total_time = end_time - start_time print(f"Total ingestion time: {total_time}")
  2. ctx:claims/beam/f6d6e5e8-2e81-4b5b-8ad1-a93a9616694c
    • full textbeam-chunk
      text/plain1 KBdoc:beam/f6d6e5e8-2e81-4b5b-8ad1-a93a9616694c
      Show excerpt
      return 1 - accuracy # Convert RMSE to accuracy-like metric # Load the test interactions interactions = np.load("interactions.npy") # Define the reader and load the dataset reader = Reader(rating_scale=(1, 5)) # Adjust the rating sca
  3. ctx:claims/beam/1fc14f37-f4dc-462b-8ced-d7ac65395d13
    • full textbeam-chunk
      text/plain1 KBdoc:beam/1fc14f37-f4dc-462b-8ced-d7ac65395d13
      Show excerpt
      Ensure your CI/CD pipeline runs the Python script and logs the metrics to the specified file. Here's an example GitHub Actions workflow: ```yaml name: CI/CD Pipeline on: push: branches: - main pull_request: branches:

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