Dontopedia

Evaluate Function

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-09.)

Evaluate Function has 23 facts recorded in Dontopedia across 3 references, with 3 live disagreements.

23 facts·16 predicates·3 sources·3 in dispute

Mostly:calls function(4), returns(4), has parameter(2)

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.

isExternalDependencyIs External Dependency(1)

Other facts (23)

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.

23 facts
PredicateValueRef
Calls FunctionAccuracy Score[3]
Calls FunctionPrecision Score[3]
Calls FunctionRecall Score[3]
Calls FunctionF1 Score[3]
ReturnsAccuracy Value[3]
ReturnsPrecision Value[3]
ReturnsRecall Value[3]
ReturnsF1 Value[3]
Has ParameterY True[3]
Has ParameterY Pred[3]
Uses Broken Dataloadertrue[1]
Inherits Bug FromCreate Dataloader Function[1]
Uses Broken ComponentCreate Dataloader[2]
Rdf:typeEvaluation Function[3]
Inverse ofEvaluation Metric Computation[3]
Returns TupleMetric Tuple[3]
Is Utility FunctionTrue[3]
Computes MetricsModel Performance[3]
Computes AccuracyAcc[3]
Computes PrecisionPrec[3]
Computes RecallRec[3]
Computes F1F1[3]
Is for Model EvaluationTrue[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.

usesBrokenDataloaderblah/resources/part-43
true
inheritsBugFromblah/resources/part-43
ex:create-dataloader-function
usesBrokenComponentblah/resources/43
ex:create_dataloader
typebeam/42f279b2-a34b-446e-9204-29e263d7a929
ex:EvaluationFunction
hasParameterbeam/42f279b2-a34b-446e-9204-29e263d7a929
ex:y-true
hasParameterbeam/42f279b2-a34b-446e-9204-29e263d7a929
ex:y-pred
callsFunctionbeam/42f279b2-a34b-446e-9204-29e263d7a929
ex:accuracy-score
callsFunctionbeam/42f279b2-a34b-446e-9204-29e263d7a929
ex:precision-score
callsFunctionbeam/42f279b2-a34b-446e-9204-29e263d7a929
ex:recall-score
callsFunctionbeam/42f279b2-a34b-446e-9204-29e263d7a929
ex:f1-score
returnsbeam/42f279b2-a34b-446e-9204-29e263d7a929
ex:accuracy-value
returnsbeam/42f279b2-a34b-446e-9204-29e263d7a929
ex:precision-value
returnsbeam/42f279b2-a34b-446e-9204-29e263d7a929
ex:recall-value
returnsbeam/42f279b2-a34b-446e-9204-29e263d7a929
ex:f1-value
inverseOfbeam/42f279b2-a34b-446e-9204-29e263d7a929
ex:evaluation-metric-computation
returnsTuplebeam/42f279b2-a34b-446e-9204-29e263d7a929
ex:metric-tuple
isUtilityFunctionbeam/42f279b2-a34b-446e-9204-29e263d7a929
ex:true
computesMetricsbeam/42f279b2-a34b-446e-9204-29e263d7a929
ex:model-performance
computesAccuracybeam/42f279b2-a34b-446e-9204-29e263d7a929
ex:acc
computesPrecisionbeam/42f279b2-a34b-446e-9204-29e263d7a929
ex:prec
computesRecallbeam/42f279b2-a34b-446e-9204-29e263d7a929
ex:rec
computesF1beam/42f279b2-a34b-446e-9204-29e263d7a929
ex:f1
isForModelEvaluationbeam/42f279b2-a34b-446e-9204-29e263d7a929
ex:true

References (3)

3 references
  1. [1]Part 432 facts
    ctx:discord/blah/resources/part-43
  2. [2]431 fact
    ctx:discord/blah/resources/43
    • full textresources-43
      text/plain3 KBdoc:agent/resources-43/a256cf99-d471-4271-ae77-eb840b3f966a
      Show excerpt
      [2026-02-28 16:44] xenonfun: (files: 833a969e-5e24-435d-9e4e-43c2cbc3e723.png) [2026-02-28 23:54] xenonfun: `mlx-community/Qwen3.5-35B-A3B-8bit` test: The model finished. Here's my grade: --- Grade: 6.5 / 10 What it got right -
  3. ctx:claims/beam/42f279b2-a34b-446e-9204-29e263d7a929
    • full textbeam-chunk
      text/plain1 KBdoc:beam/42f279b2-a34b-446e-9204-29e263d7a929
      Show excerpt
      from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score def evaluate(y_true, y_pred): acc = accuracy_score(y_true, y_pred) prec = precision_score(y_true, y_pred, average='weighted')

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