Class: Aws::Comprehend::Types::EntityRecognizerEvaluationMetrics
- Inherits:
-
Struct
- Object
- Struct
- Aws::Comprehend::Types::EntityRecognizerEvaluationMetrics
- Includes:
- Structure
- Defined in:
- lib/aws-sdk-comprehend/types.rb
Overview
Detailed information about the accuracy of an entity recognizer.
Constant Summary collapse
- SENSITIVE =
[]
Instance Attribute Summary collapse
-
#f1_score ⇒ Float
A measure of how accurate the recognizer results are for the test data.
-
#precision ⇒ Float
A measure of the usefulness of the recognizer results in the test data.
-
#recall ⇒ Float
A measure of how complete the recognizer results are for the test data.
Instance Attribute Details
#f1_score ⇒ Float
A measure of how accurate the recognizer 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. For plain text entity recognizer models, the range is 0 to 100, where 100 is the best score. For PDF/Word entity recognizer models, the range is 0 to 1, where 1 is the best score.
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# File 'lib/aws-sdk-comprehend/types.rb', line 4071 class EntityRecognizerEvaluationMetrics < Struct.new( :precision, :recall, :f1_score) SENSITIVE = [] include Aws::Structure end |
#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.
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# File 'lib/aws-sdk-comprehend/types.rb', line 4071 class EntityRecognizerEvaluationMetrics < Struct.new( :precision, :recall, :f1_score) SENSITIVE = [] include Aws::Structure end |
#recall ⇒ Float
A measure of how complete the recognizer results are for the test data. High recall means that the recognizer returned most of the relevant results.
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# File 'lib/aws-sdk-comprehend/types.rb', line 4071 class EntityRecognizerEvaluationMetrics < Struct.new( :precision, :recall, :f1_score) SENSITIVE = [] include Aws::Structure end |