dwc:measurementAccuracy

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. Accuracy is a metric used in the context of classification tasks to evaluate the proportion of correctly predicted instances among the total instances. Key Points: Use Case: Classification performance evaluation. Metric: Measures the proportion of correct predictions. Interpretation: Higher values indicate better classification performance.

Examples

  • 0.01
  • normal distribution with variation of 2 m

References

  1. Wieczorek J., Bloom D., Guralnick R., Blum S., Döring M., Giovanni R. et al. 2012 Darwin Core: An Evolving Community-Developed Biodiversity Data Standard. PLOS ONE. Vol. 7, (1) p. e29715. doi: 10.1371/journal.pone.0029715
  2. STATO 2025 STATistics Ontology (STATO). GitHub - ISA-tools. [2025-2-8] github.com

Related Data

Columns

No descriptor relatables

Properties

Label

measurementAccuracy

EUPH UID

[0.0.MSRMN782]

IRI

http://rs.tdwg.org/dwc/terms/measurementAccuracy

EUPH IRI

https://app.pollinatorhub.eu/vocabulary/descriptors/0.0.MSRMN782

Data types

Integer, Decimal (float), String

Unit

No unit

Deprecated

No

Published

2025-01-24

Updated

2025-01-24
Descriptor relationships

Namespace

dwc

Class

Dependencies

Statistic

Number of datasets

3

Number of columns

6

Number of data points

0

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