Detailed information about the accuracy of an entity recognizer.
Type: objectProperties: 3
Machine-LearningNatural Language ProcessingNLPText Analysis
EntityRecognizerEvaluationMetrics is a JSON Structure definition published by Amazon Comprehend, describing 3 properties. It conforms to the https://json-structure.org/meta/core/v0/# meta-schema.
{
"$schema": "https://json-structure.org/meta/core/v0/#",
"$id": "https://raw.githubusercontent.com/api-evangelist/amazon-comprehend/refs/heads/main/json-structure/openapi.yml-entity-recognizer-evaluation-metrics-structure.json",
"name": "EntityRecognizerEvaluationMetrics",
"description": "Detailed information about the accuracy of an entity recognizer. ",
"type": "object",
"properties": {
"Precision": {
"allOf": [
{
"$ref": "#/components/schemas/Double"
},
{
"description": "A measure of the usefulness of the recognizer results in the test data. High precision means that the recognizer returned substantially more relevant results than irrelevant ones. "
}
]
},
"Recall": {
"allOf": [
{
"$ref": "#/components/schemas/Double"
},
{
"description": "A measure of how complete the recognizer results are for the test data. High recall means that the recognizer returned most of the relevant results."
}
]
},
"F1Score": {
"allOf": [
{
"$ref": "#/components/schemas/Double"
},
{
"description": "A measure of how accurate the recognizer results are for the test data. It is derived from the <code>Precision</code> and <code>Recall</code> values. The <code>F1Score</code> is the harmonic average of the two scores. For plain text entity recognizer models, the range is 0 to 100, where 100 is the best score. For PDF/Word entity recognizer models, the range is 0 to 1, where 1 is the best score. "
}
]
}
}
}
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