Evaluation Code

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From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-11.)

Evaluation Code has 24 facts recorded in Dontopedia across 4 references, with 5 live disagreements.

24 facts·12 predicates·4 sources·5 in dispute

Mostly:imports(8), rdf:type(3), calculates metric(2)

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.

`contains`Contains(2)

Code Blockex:code-block
Search Improvement Workflowex:search-improvement-workflow

`describes`Describes(1)

Code Incompletenessex:code-incompleteness

`isLocatedAfter`Is Located After(1)

Summary Sectionsummary-section

Other facts (24)

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.

24 facts

Predicate	Value	Ref
Imports	Numpy	[1]
Imports	Sklearn Cluster	[1]
Imports	Sklearn Metrics	[1]
Imports	Sklearn Preprocessing	[1]
Imports	Sklearn Datasets	[1]
Imports	Precision Score	[3]
Imports	Recall Score	[3]
Imports	Train Test Split	[3]
Rdf:type	Python Code	[1]
Rdf:type	Missing Code	[2]
Rdf:type	Code Block	[4]
Calculates Metric	Precision	[4]
Calculates Metric	Recall	[4]
Extends	Y True	[4]
Extends	Y Pred	[4]
Calculates	Precision Score	[4]
Calculates	Recall Score	[4]
Programming Language	Python	[1]
Expected to Use	accuracy_score	[2]
Contains	Dataset Loading Section	[3]
Prints Result	Precision and Recall	[4]
Is Part of	Search Improvement Workflow	[4]
Is Written in	Python	[4]
Belongs to Intent	Search Improvement Workflow	[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.

24 undated

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typebeam/150d3ab0-4c59-4efc-b47d-5284bb249422

ex:PythonCode

—

importsbeam/150d3ab0-4c59-4efc-b47d-5284bb249422

ex:numpy

—

importsbeam/150d3ab0-4c59-4efc-b47d-5284bb249422

ex:sklearn-cluster

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importsbeam/150d3ab0-4c59-4efc-b47d-5284bb249422

ex:sklearn-metrics

—

importsbeam/150d3ab0-4c59-4efc-b47d-5284bb249422

ex:sklearn-preprocessing

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importsbeam/150d3ab0-4c59-4efc-b47d-5284bb249422

ex:sklearn-datasets

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programmingLanguagebeam/150d3ab0-4c59-4efc-b47d-5284bb249422

ex:Python

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typebeam/40ad9efd-31cb-4009-8b35-e5d32e632e93

ex:missing-code

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expectedToUsebeam/40ad9efd-31cb-4009-8b35-e5d32e632e93

accuracy_score

—

importsbeam/34a1dce2-ecc2-4241-ad4a-235e8625b612

ex:precision-score

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importsbeam/34a1dce2-ecc2-4241-ad4a-235e8625b612

ex:recall-score

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importsbeam/34a1dce2-ecc2-4241-ad4a-235e8625b612

ex:train-test-split

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containsbeam/34a1dce2-ecc2-4241-ad4a-235e8625b612

ex:dataset-loading-section

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calculatesMetricbeam/4b0e94ef-084d-4363-8931-568f755392e6

ex:precision

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calculatesMetricbeam/4b0e94ef-084d-4363-8931-568f755392e6

ex:recall

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printsResultbeam/4b0e94ef-084d-4363-8931-568f755392e6

ex:precision-and-recall

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extendsbeam/4b0e94ef-084d-4363-8931-568f755392e6

ex:y-true

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extendsbeam/4b0e94ef-084d-4363-8931-568f755392e6

ex:y-pred

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typebeam/4b0e94ef-084d-4363-8931-568f755392e6

ex:CodeBlock

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calculatesbeam/4b0e94ef-084d-4363-8931-568f755392e6

ex:precision-score

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calculatesbeam/4b0e94ef-084d-4363-8931-568f755392e6

ex:recall-score

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isPartOfbeam/4b0e94ef-084d-4363-8931-568f755392e6

ex:search-improvement-workflow

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isWrittenInbeam/4b0e94ef-084d-4363-8931-568f755392e6

ex:python

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belongsToIntentbeam/4b0e94ef-084d-4363-8931-568f755392e6

ex:search-improvement-workflow

References (4)

4 references

[1]150d3ab0 4c59 4efc B47d 5284bb2494227 facts
ctx:claims/beam/150d3ab0-4c59-4efc-b47d-5284bb249422
- full textbeam-chunk
  text/plain1 KBdoc:beam/150d3ab0-4c59-4efc-b47d-5284bb249422
  Show excerpt
  [Turn 503] Assistant: To determine which clustering algorithm performed the best based on the silhouette score, you would need to run the provided code and compare the silhouette scores for each algorithm. The silhouette score ranges from -…
[2]40ad9efd 31cb 4009 8b35 E5d32e632e932 facts
ctx:claims/beam/40ad9efd-31cb-4009-8b35-e5d32e632e93
- full textbeam-chunk
  text/plain1 KBdoc:beam/40ad9efd-31cb-4009-8b35-e5d32e632e93
  Show excerpt
  - Review the logs and debugging output to identify the root cause of the issue. ### Example Implementation Let's assume you have an evaluation pipeline that uses Scikit-learn for model evaluation. We'll add detailed logging and use `pd…
[3]34a1dce2 Ecc2 4241 Ad4a 235e8625b6124 facts
ctx:claims/beam/34a1dce2-ecc2-4241-ad4a-235e8625b612
- full textbeam-chunk
  text/plain1 KBdoc:beam/34a1dce2-ecc2-4241-ad4a-235e8625b612
  Show excerpt
  retrieved_documents = rag_system.process_query(reformulated_query, context) return reformulated_query, retrieved_documents # Apply the function to each row df[['reformulated_query', 'retrieved_documents']] = df.apply( lambda ro…
[4]4b0e94ef 084d 4363 8931 568f755392e611 facts
ctx:claims/beam/4b0e94ef-084d-4363-8931-568f755392e6
- full textbeam-chunk
  text/plain1 KBdoc:beam/4b0e94ef-084d-4363-8931-568f755392e6
  Show excerpt
  true_vector = [doc in ground_truth_documents for doc in retrieved_documents] pred_vector = [True] * len(retrieved_documents) y_true.extend(true_vector) y_pred.extend(pred_vector) # Calculate precision and recall precision …

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