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- 20260323T101847-16 type Variable assertion.
- 20260323T101847-16 type FAIRDigitalObject assertion.
- 20260323T101847-16 comment "LLM-proposed preferred label is stored in skos:prefLabel. The alternative label is generated from the simple-entity formula." assertion.
- 20260323T101847-16 identifier "iadopt-variable-20260323T101847-16" assertion.
- 20260323T101847-16 definition "The F1 score is a robust machine learning evaluation metric for classification models that acts as the harmonic mean of precision and recall. Ranging from 0 to 1, it provides a balanced performance score, especially valuable when dealing with imbalanced datasets where accuracy is misleading. A high F1 score indicates high precision (few false positives) and high recall (few false negatives) simultaneously." assertion.
- 20260323T101847-16 hasConstraint _ndaadab1399da457ba273c673dd8f3345b1 assertion.
- 20260323T101847-16 hasConstraint _ndaadab1399da457ba273c673dd8f3345b2 assertion.
- 20260323T101847-16 hasConstraint _ndaadab1399da457ba273c673dd8f3345b3 assertion.
- 20260323T101847-16 hasConstraint _ndaadab1399da457ba273c673dd8f3345b4 assertion.
- 20260323T101847-16 hasObjectOfInterest hasObjectOfInterest assertion.
- 20260323T101847-16 hasProperty hasProperty assertion.
- 20260323T101847-16 conformsTo RA5MTl9GFH-QuuBHYEA2hOtxOMOV4-jrhtdx5lOy9CAQE assertion.
- 20260323T101847-16 prefLabel "F1 score" assertion.
- 20260323T101847-16 altLabel "range: 0 to 1 composition: harmonic mean of precision and recall score of classification models type: classification models condition: imbalanced datasets" assertion.