Dontopedia

accuracy1

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

accuracy1 has 9 facts recorded in Dontopedia across 1 reference.

9 facts·8 predicates·1 sources

Mostly:is calculated by(1), is variable(1), rdf:type(1)

Maturity scale raw canonical shape-checked rule-derived certified

Inbound mentions (1)

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.

dependsOnDepends on(1)

Other facts (8)

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.

8 facts
PredicateValueRef
Is Calculated byNp Mean Argmax Comparison[1]
Is VariableAccuracy Metric[1]
Rdf:typeAccuracy Metric[1]
Depends onScores1[1]
Is Calculated BeforeNew Weights[1]
Has Value0.6666666666666666[1]
Uses FunctionNp Mean[1]
Uses ArgmaxScores1[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.

isCalculatedBybeam/7c39567a-d596-4c72-aa0d-d70287a5c1e4
ex:np_mean_argmax_comparison
isVariablebeam/7c39567a-d596-4c72-aa0d-d70287a5c1e4
ex:accuracy_metric
typebeam/7c39567a-d596-4c72-aa0d-d70287a5c1e4
ex:AccuracyMetric
labelbeam/7c39567a-d596-4c72-aa0d-d70287a5c1e4
accuracy1
dependsOnbeam/7c39567a-d596-4c72-aa0d-d70287a5c1e4
ex:scores1
isCalculatedBeforebeam/7c39567a-d596-4c72-aa0d-d70287a5c1e4
ex:new_weights
hasValuebeam/7c39567a-d596-4c72-aa0d-d70287a5c1e4
0.6666666666666666
usesFunctionbeam/7c39567a-d596-4c72-aa0d-d70287a5c1e4
ex:np_mean
usesArgmaxbeam/7c39567a-d596-4c72-aa0d-d70287a5c1e4
ex:scores1

References (1)

1 references
  1. ctx:claims/beam/7c39567a-d596-4c72-aa0d-d70287a5c1e4
    • full textbeam-chunk
      text/plain1 KBdoc:beam/7c39567a-d596-4c72-aa0d-d70287a5c1e4
      Show excerpt
      # Calculate accuracy for each engine accuracy1 = np.mean(np.argmax(scores1, axis=1) == true_labels) accuracy2 = np.mean(np.argmax(scores2, axis=1) == true_labels) # Update weights based on accuracy new_weights = (ac

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