You are viewing documentation for version 2 of the AWS SDK for Ruby. Version 3 documentation can be found here.
Class: Aws::Comprehend::Types::ClassifierEvaluationMetrics
 Inherits:

Struct
 Object
 Struct
 Aws::Comprehend::Types::ClassifierEvaluationMetrics
 Defined in:
 (unknown)
Overview
Describes the result metrics for the test data associated with an documentation classifier.
Returned by:
Instance Attribute Summary collapse

#accuracy ⇒ Float
The fraction of the labels that were correct recognized.

#f1_score ⇒ Float
A measure of how accurate the classifier results are for the test data.

#hamming_loss ⇒ Float
Indicates the fraction of labels that are incorrectly predicted.

#micro_f1_score ⇒ Float
A measure of how accurate the classifier results are for the test data.

#micro_precision ⇒ Float
A measure of the usefulness of the recognizer results in the test data.

#micro_recall ⇒ Float
A measure of how complete the classifier results are for the test data.

#precision ⇒ Float
A measure of the usefulness of the classifier results in the test data.

#recall ⇒ Float
A measure of how complete the classifier results are for the test data.
Instance Attribute Details
#accuracy ⇒ Float
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.
#f1_score ⇒ Float
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.
#hamming_loss ⇒ Float
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.
#micro_f1_score ⇒ Float
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.
#micro_precision ⇒ Float
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.
#micro_recall ⇒ Float
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 ⇒ Float
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 ⇒ Float
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.