Metric Functions
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-10.)
Metric Functions has 9 facts recorded in Dontopedia across 4 references, with 2 live disagreements.
Mostly:includes(4), rdf:type(2), collectively called(1)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (5)
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.
partOfPart of(4)
- Average Precision Score Func
ex:average-precision-score-func - F1 Score Func
ex:f1-score-func - Precision Score Func
ex:precision-score-func - Recall Score Func
ex:recall-score-func
providesProvides(1)
- Sklearn Import
ex:sklearn-import
Other facts (9)
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.
| Predicate | Value | Ref |
|---|---|---|
| Includes | Precision Score | [3] |
| Includes | Recall Score | [3] |
| Includes | F1 Score | [3] |
| Includes | Accuracy Score | [3] |
| Rdf:type | Sklearn Functions | [1] |
| Rdf:type | Evaluation Functions | [1] |
| Collectively Called | sklearn metrics | [2] |
| Common Signature | Predictions True Labels Weights | [3] |
| Assumes | sorted-predictions | [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.
References (4)
ctx:claims/beam/dfbb9e1e-3e56-4d8e-b41d-1a690438b469ctx:claims/beam/c07ae379-ae89-4db6-8cc7-34e24961d945ctx:claims/beam/a55e7e9c-f5ae-4d91-b7ce-cd62d5497865ctx:claims/beam/7c7c4d94-1626-4327-b6b2-b57b1fc421dd- full textbeam-chunktext/plain1 KB
doc:beam/7c7c4d94-1626-4327-b6b2-b57b1fc421ddShow excerpt
num_queries = 1000 num_items = 10 # Generate random predictions and labels predictions = np.random.rand(num_queries, num_items) labels = np.random.randint(0, 2, size=(num_queries, num_items)) # Calculate metrics for each query ndcg_values…
See also
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