Full identifier: http://purl.obolibrary.org/obo/STATO_0000415
(none)
| Nanopublication | Part | Subject | Predicate | Object | Published By | Published On |
|---|---|---|---|---|---|---|
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links a nanopublication to its assertion
http://www.nanopub.org/nschema#hasAssertion
assertion
|
STATO_0000415
|
(unknown)
|
2025-03-27T14:31:19.753Z
|
|||
|
links a nanopublication to its pubinfo
http://www.nanopub.org/nschema#hasPublicationInfo
pubinfo
|
STATO_0000415
|
accuracy - in the context of binary classification, accuracy is defined as the proportion of true results (both true positives and true negatives) to the total number of cases examined (the sum of true positive, true negative, false positive and false negative).
It can be understood as a measure of the proximity of measurement results to the true value.
|
Tobias Kuhn
|
2023-08-21T13:04:28.171Z
|
||
|
links a nanopublication to its assertion
http://www.nanopub.org/nschema#hasAssertion
assertion
|
STATO_0000415
|
Tobias Kuhn
|
2023-08-21T13:04:28.171Z
|
|||
|
links a nanopublication to its pubinfo
http://www.nanopub.org/nschema#hasPublicationInfo
pubinfo
|
STATO_0000415
|
accuracy - in the context of binary classification, accuracy is defined as the proportion of true results (both true positives and true negatives) to the total number of cases examined (the sum of true positive, true negative, false positive and false negative).
It can be understood as a measure of the proximity of measurement results to the true value.
|
Tobias Kuhn
|
2023-08-21T13:00:58.988Z
|
||
|
links a nanopublication to its assertion
http://www.nanopub.org/nschema#hasAssertion
assertion
|
STATO_0000415
|
Tobias Kuhn
|
2023-08-21T13:00:58.988Z
|
|||
|
links a nanopublication to its pubinfo
http://www.nanopub.org/nschema#hasPublicationInfo
pubinfo
|
STATO_0000415
|
accuracy - in the context of binary classification, accuracy is defined as the proportion of true results (both true positives and true negatives) to the total number of cases examined (the sum of true positive, true negative, false positive and false negative).
It can be understood as a measure of the proximity of measurement results to the true value.
|
Tobias Kuhn
|
2023-08-21T12:36:21.322Z
|
||
|
links a nanopublication to its assertion
http://www.nanopub.org/nschema#hasAssertion
assertion
|
STATO_0000415
|
Tobias Kuhn
|
2023-08-21T12:36:21.322Z
|
|||
|
links a nanopublication to its pubinfo
http://www.nanopub.org/nschema#hasPublicationInfo
pubinfo
|
STATO_0000415
|
accuracy - in the context of binary classification, accuracy is defined as the proportion of true results (both true positives and true negatives) to the total number of cases examined (the sum of true positive, true negative, false positive and false negative).
It can be understood as a measure of the proximity of measurement results to the true value.
|
Tobias Kuhn
|
2023-08-21T12:34:49.052Z
|
||
|
links a nanopublication to its assertion
http://www.nanopub.org/nschema#hasAssertion
assertion
|
STATO_0000415
|
Tobias Kuhn
|
2023-08-21T12:34:49.052Z
|
|||
|
links a nanopublication to its assertion
http://www.nanopub.org/nschema#hasAssertion
assertion
|
STATO_0000415
|
accuracy - in the context of binary classification, accuracy is defined as the proportion of true results (both true positives and true negatives) to the total number of cases examined (the sum of true positive, true negative, false positive and false negative). It can be understood as a measure of the proximity of measurement results to the true value.
|
Tobias Kuhn
|
2023-08-16T11:05:20.868Z
|
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