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ClassifierMetadata

Source code in docs_src/capo_comprehend/types/classifier_metadata.py 
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class ClassifierMetadata(TypedDict, closed=True):
    number_of_labels: NotRequired["capo_comprehend.types.integer.Integer"]
    """<p>The number of labels in the input data. </p>"""
    number_of_trained_documents: NotRequired["capo_comprehend.types.integer.Integer"]
    """<p>The number of documents in the input data that were used to train the classifier. Typically this is 80 to 90 percent of the input documents.</p>"""
    number_of_test_documents: NotRequired["capo_comprehend.types.integer.Integer"]
    """<p>The number of documents in the input data that were used to test the classifier. Typically this is 10 to 20 percent of the input documents, up to 10,000 documents.</p>"""
    evaluation_metrics: NotRequired[
        "capo_comprehend.types.classifier_evaluation_metrics.ClassifierEvaluationMetrics"
    ]
    """<p> Describes the result metrics for the test data associated with an documentation classifier.</p>"""

evaluation_metrics instance-attribute 

evaluation_metrics: NotRequired[ClassifierEvaluationMetrics]

Describes the result metrics for the test data associated with an documentation classifier.

number_of_labels instance-attribute 

number_of_labels: NotRequired[Integer]

The number of labels in the input data.

number_of_test_documents instance-attribute 

number_of_test_documents: NotRequired[Integer]

The number of documents in the input data that were used to test the classifier. Typically this is 10 to 20 percent of the input documents, up to 10,000 documents.

number_of_trained_documents instance-attribute 

number_of_trained_documents: NotRequired[Integer]

The number of documents in the input data that were used to train the classifier. Typically this is 80 to 90 percent of the input documents.