Dontopedia

Rmse

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

Rmse has 8 facts recorded in Dontopedia across 2 references.

8 facts·8 predicates·2 sources

Mostly:full name(1), rdf:type(1), is used for(1)

Maturity scale raw canonical shape-checked rule-derived certified

Full NamefullName

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.

usesMetricUses Metric(3)

isDerivedFromIs Derived From(1)

Other facts (7)

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.

7 facts
PredicateValueRef
Rdf:typeError Metric[1]
Is Used forEvaluate Performance[1]
NamespaceSurprise Accuracy[2]
Function CallSurprise Accuracy.rmse[2]
Metric TypeRegression Metric[2]
CalculationRoot Mean Square Deviation[2]
Metric CategoryError Metric[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/1da05a31-8d6c-42fb-be75-de09a6b68622
ex:ErrorMetric
isUsedForbeam/1da05a31-8d6c-42fb-be75-de09a6b68622
ex:evaluate_performance
fullNamebeam/bb48cb28-dac4-4e76-8054-489138e7e97f
ex:RootMeanSquareError
namespacebeam/bb48cb28-dac4-4e76-8054-489138e7e97f
ex:surprise-accuracy
functionCallbeam/bb48cb28-dac4-4e76-8054-489138e7e97f
ex:surprise_accuracy.rmse
metricTypebeam/bb48cb28-dac4-4e76-8054-489138e7e97f
ex:RegressionMetric
calculationbeam/bb48cb28-dac4-4e76-8054-489138e7e97f
ex:RootMeanSquareDeviation
metricCategorybeam/bb48cb28-dac4-4e76-8054-489138e7e97f
ex:ErrorMetric

References (2)

2 references
  1. ctx:claims/beam/1da05a31-8d6c-42fb-be75-de09a6b68622
    • full textbeam-chunk
      text/plain1 KBdoc:beam/1da05a31-8d6c-42fb-be75-de09a6b68622
      Show excerpt
      self.partial_fit([(user_id, item_id, rating)]) # Monkey-patch the update method to the SVD class SVD.update = update # Re-test the algorithm with relevance scores accuracy_with_relevance = test_algorithm(feedback_loop_algorithm, i
  2. ctx:claims/beam/bb48cb28-dac4-4e76-8054-489138e7e97f

See also

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