Class: Aws::Comprehend::Types::ClassifierEvaluationMetrics
- Inherits:
-
Struct
- Object
- Struct
- Aws::Comprehend::Types::ClassifierEvaluationMetrics
- Defined in:
- gems/aws-sdk-comprehend/lib/aws-sdk-comprehend/types.rb
Overview
Describes the result metrics for the test data associated with an documentation classifier.
Constant Summary collapse
- SENSITIVE =
[]
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.
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# File 'gems/aws-sdk-comprehend/lib/aws-sdk-comprehend/types.rb', line 728 class ClassifierEvaluationMetrics < Struct.new( :accuracy, :precision, :recall, :f1_score, :micro_precision, :micro_recall, :micro_f1_score, :hamming_loss) SENSITIVE = [] include Aws::Structure end |
#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.
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# File 'gems/aws-sdk-comprehend/lib/aws-sdk-comprehend/types.rb', line 728 class ClassifierEvaluationMetrics < Struct.new( :accuracy, :precision, :recall, :f1_score, :micro_precision, :micro_recall, :micro_f1_score, :hamming_loss) SENSITIVE = [] include Aws::Structure end |
#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.
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# File 'gems/aws-sdk-comprehend/lib/aws-sdk-comprehend/types.rb', line 728 class ClassifierEvaluationMetrics < Struct.new( :accuracy, :precision, :recall, :f1_score, :micro_precision, :micro_recall, :micro_f1_score, :hamming_loss) SENSITIVE = [] include Aws::Structure end |
#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.
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# File 'gems/aws-sdk-comprehend/lib/aws-sdk-comprehend/types.rb', line 728 class ClassifierEvaluationMetrics < Struct.new( :accuracy, :precision, :recall, :f1_score, :micro_precision, :micro_recall, :micro_f1_score, :hamming_loss) SENSITIVE = [] include Aws::Structure end |
#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.
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# File 'gems/aws-sdk-comprehend/lib/aws-sdk-comprehend/types.rb', line 728 class ClassifierEvaluationMetrics < Struct.new( :accuracy, :precision, :recall, :f1_score, :micro_precision, :micro_recall, :micro_f1_score, :hamming_loss) SENSITIVE = [] include Aws::Structure end |
#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.
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# File 'gems/aws-sdk-comprehend/lib/aws-sdk-comprehend/types.rb', line 728 class ClassifierEvaluationMetrics < Struct.new( :accuracy, :precision, :recall, :f1_score, :micro_precision, :micro_recall, :micro_f1_score, :hamming_loss) SENSITIVE = [] include Aws::Structure end |
#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.
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# File 'gems/aws-sdk-comprehend/lib/aws-sdk-comprehend/types.rb', line 728 class ClassifierEvaluationMetrics < Struct.new( :accuracy, :precision, :recall, :f1_score, :micro_precision, :micro_recall, :micro_f1_score, :hamming_loss) SENSITIVE = [] include Aws::Structure end |
#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.
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# File 'gems/aws-sdk-comprehend/lib/aws-sdk-comprehend/types.rb', line 728 class ClassifierEvaluationMetrics < Struct.new( :accuracy, :precision, :recall, :f1_score, :micro_precision, :micro_recall, :micro_f1_score, :hamming_loss) SENSITIVE = [] include Aws::Structure end |