NAME

Paws::Comprehend::ClassifierEvaluationMetrics

USAGE

This class represents one of two things:

Arguments in a call to a service

Use the attributes of this class as arguments to methods. You shouldn't make instances of this class. Each attribute should be used as a named argument in the calls that expect this type of object.

As an example, if Att1 is expected to be a Paws::Comprehend::ClassifierEvaluationMetrics object:

  $service_obj->Method(Att1 => { Accuracy => $value, ..., Recall => $value  });

Results returned from an API call

Use accessors for each attribute. If Att1 is expected to be an Paws::Comprehend::ClassifierEvaluationMetrics object:

  $result = $service_obj->Method(...);
  $result->Att1->Accuracy

DESCRIPTION

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

ATTRIBUTES

Accuracy => Num

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.

F1Score => Num

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.

HammingLoss => Num

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.

MicroF1Score => Num

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.

MicroPrecision => Num

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.

MicroRecall => Num

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.

Precision => Num

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.

Recall => Num

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.

BUGS and CONTRIBUTIONS

The source code is located here: <https://github.com/pplu/aws-sdk-perl>

Please report bugs to: <https://github.com/pplu/aws-sdk-perl/issues>