class Aws::Comprehend::Types::ClassifierEvaluationMetrics

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

@!attribute [rw] accuracy

The fraction of the labels that were correct recognized. It is
computed by dividing the number of labels in the test documents that
were correctly recognized by the total number of labels in the test
documents.
@return [Float]

@!attribute [rw] precision

A measure of the usefulness of the classifier results in the test
data. High precision means that the classifier returned
substantially more relevant results than irrelevant ones.
@return [Float]

@!attribute [rw] recall

A measure of how complete the classifier results are for the test
data. High recall means that the classifier returned most of the
relevant results.
@return [Float]

@!attribute [rw] f1_score

A measure of how accurate the classifier 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. The highest
score is 1, and the worst score is 0.
@return [Float]

@!attribute [rw] micro_precision

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. Unlike the
Precision metric which comes from averaging the precision of all
available labels, this is based on the overall score of all
precision scores added together.
@return [Float]

@!attribute [rw] micro_recall

A measure of how complete the classifier results are for the test
data. High recall means that the classifier returned most of the
relevant results. Specifically, this indicates how many of the
correct categories in the text that the model can predict. It is a
percentage of correct categories in the text that can found. Instead
of averaging the recall scores of all labels (as with Recall), micro
Recall is based on the overall score of all recall scores added
together.
@return [Float]

@!attribute [rw] micro_f1_score

A measure of how accurate the classifier results are for the test
data. It is a combination of the `Micro Precision` and `Micro
Recall` values. The `Micro F1Score` is the harmonic mean of the two
scores. The highest score is 1, and the worst score is 0.
@return [Float]

@!attribute [rw] hamming_loss

Indicates the fraction of labels that are incorrectly predicted.
Also seen as the fraction of wrong labels compared to the total
number of labels. Scores closer to zero are better.
@return [Float]

@see docs.aws.amazon.com/goto/WebAPI/comprehend-2017-11-27/ClassifierEvaluationMetrics AWS API Documentation

Constants

SENSITIVE