Dontopedia

average metric accuracy

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

average metric accuracy has 4 facts recorded in Dontopedia across 2 references, with 1 live disagreement.

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

Inbound mentions (3)

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.

outputsMetricOutputs Metric(1)

referencesVariableReferences Variable(1)

returnsReturns(1)

Other facts (3)

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.

3 facts
PredicateValueRef
Rdf:typeStatistical Measure[1]
Rdf:typeCalculated Value[2]
Used inF String Format 2[2]

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/35ebfeb5-e555-48ad-a03b-b1386ef4d4d1
ex:StatisticalMeasure
typebeam/59a85bc3-c979-494e-89ab-09b065bdba25
ex:CalculatedValue
labelbeam/59a85bc3-c979-494e-89ab-09b065bdba25
average metric accuracy
usedInbeam/59a85bc3-c979-494e-89ab-09b065bdba25
ex:f-string-format-2

References (2)

2 references
  1. ctx:claims/beam/35ebfeb5-e555-48ad-a03b-b1386ef4d4d1
    • full textbeam-chunk
      text/plain1 KBdoc:beam/35ebfeb5-e555-48ad-a03b-b1386ef4d4d1
      Show excerpt
      [Turn 9306] User: I've been working on improving the metric accuracy of my evaluation pipeline, and I've seen a significant boost after tweaking the algorithm for 22,000 tests. However, I'm concerned about the potential impact of this chang
  2. ctx:claims/beam/59a85bc3-c979-494e-89ab-09b065bdba25
    • full textbeam-chunk
      text/plain1 KBdoc:beam/59a85bc3-c979-494e-89ab-09b065bdba25
      Show excerpt
      average_metric_accuracy = np.mean(metric_accuracies) logging.info(f"Processed {num_tests} tests in {elapsed_time:.2f} seconds") logging.info(f"Average metric accuracy: {average_metric_accuracy}") if __name__ == "__main__":

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.