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EntityRecognizerEvaluationMetrics

Source code in docs_src/capo_comprehend/types/entity_recognizer_evaluation_metrics.py 
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class EntityRecognizerEvaluationMetrics(TypedDict, closed=True):
    precision: NotRequired["capo_comprehend.types.double.Double"]
    """<p>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. </p>"""
    recall: NotRequired["capo_comprehend.types.double.Double"]
    """<p>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.</p>"""
    f1_score: NotRequired["capo_comprehend.types.double.Double"]
    """<p>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. </p>"""

f1_score instance-attribute 

f1_score: NotRequired[Double]

A measure of how accurate the recognizer results are for the test data. It is derived from the Precision and Recall values. The F1Score 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.

precision instance-attribute 

precision: NotRequired[Double]

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 instance-attribute 

recall: NotRequired[Double]

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.