328 lines
14 KiB
C++
328 lines
14 KiB
C++
/**
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* Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
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* SPDX-License-Identifier: Apache-2.0.
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*/
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#pragma once
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#include <aws/comprehend/Comprehend_EXPORTS.h>
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namespace Aws
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{
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namespace Utils
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{
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namespace Json
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{
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class JsonValue;
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class JsonView;
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} // namespace Json
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} // namespace Utils
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namespace Comprehend
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{
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namespace Model
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{
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/**
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* <p>Describes the result metrics for the test data associated with an
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* documentation classifier.</p><p><h3>See Also:</h3> <a
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* href="http://docs.aws.amazon.com/goto/WebAPI/comprehend-2017-11-27/ClassifierEvaluationMetrics">AWS
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* API Reference</a></p>
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*/
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class AWS_COMPREHEND_API ClassifierEvaluationMetrics
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{
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public:
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ClassifierEvaluationMetrics();
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ClassifierEvaluationMetrics(Aws::Utils::Json::JsonView jsonValue);
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ClassifierEvaluationMetrics& operator=(Aws::Utils::Json::JsonView jsonValue);
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Aws::Utils::Json::JsonValue Jsonize() const;
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/**
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* <p>The fraction of the labels that were correct recognized. It is computed by
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* dividing the number of labels in the test documents that were correctly
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* recognized by the total number of labels in the test documents.</p>
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*/
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inline double GetAccuracy() const{ return m_accuracy; }
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/**
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* <p>The fraction of the labels that were correct recognized. It is computed by
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* dividing the number of labels in the test documents that were correctly
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* recognized by the total number of labels in the test documents.</p>
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*/
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inline bool AccuracyHasBeenSet() const { return m_accuracyHasBeenSet; }
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/**
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* <p>The fraction of the labels that were correct recognized. It is computed by
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* dividing the number of labels in the test documents that were correctly
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* recognized by the total number of labels in the test documents.</p>
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*/
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inline void SetAccuracy(double value) { m_accuracyHasBeenSet = true; m_accuracy = value; }
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/**
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* <p>The fraction of the labels that were correct recognized. It is computed by
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* dividing the number of labels in the test documents that were correctly
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* recognized by the total number of labels in the test documents.</p>
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*/
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inline ClassifierEvaluationMetrics& WithAccuracy(double value) { SetAccuracy(value); return *this;}
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/**
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* <p>A measure of the usefulness of the classifier results in the test data. High
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* precision means that the classifier returned substantially more relevant results
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* than irrelevant ones.</p>
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*/
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inline double GetPrecision() const{ return m_precision; }
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/**
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* <p>A measure of the usefulness of the classifier results in the test data. High
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* precision means that the classifier returned substantially more relevant results
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* than irrelevant ones.</p>
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*/
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inline bool PrecisionHasBeenSet() const { return m_precisionHasBeenSet; }
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/**
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* <p>A measure of the usefulness of the classifier results in the test data. High
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* precision means that the classifier returned substantially more relevant results
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* than irrelevant ones.</p>
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*/
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inline void SetPrecision(double value) { m_precisionHasBeenSet = true; m_precision = value; }
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/**
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* <p>A measure of the usefulness of the classifier results in the test data. High
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* precision means that the classifier returned substantially more relevant results
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* than irrelevant ones.</p>
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*/
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inline ClassifierEvaluationMetrics& WithPrecision(double value) { SetPrecision(value); return *this;}
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/**
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* <p>A measure of how complete the classifier results are for the test data. High
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* recall means that the classifier returned most of the relevant results. </p>
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*/
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inline double GetRecall() const{ return m_recall; }
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/**
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* <p>A measure of how complete the classifier results are for the test data. High
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* recall means that the classifier returned most of the relevant results. </p>
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*/
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inline bool RecallHasBeenSet() const { return m_recallHasBeenSet; }
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/**
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* <p>A measure of how complete the classifier results are for the test data. High
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* recall means that the classifier returned most of the relevant results. </p>
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*/
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inline void SetRecall(double value) { m_recallHasBeenSet = true; m_recall = value; }
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/**
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* <p>A measure of how complete the classifier results are for the test data. High
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* recall means that the classifier returned most of the relevant results. </p>
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*/
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inline ClassifierEvaluationMetrics& WithRecall(double value) { SetRecall(value); return *this;}
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/**
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* <p>A measure of how accurate the classifier results are for the test data. It is
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* derived from the <code>Precision</code> and <code>Recall</code> values. The
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* <code>F1Score</code> is the harmonic average of the two scores. The highest
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* score is 1, and the worst score is 0. </p>
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*/
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inline double GetF1Score() const{ return m_f1Score; }
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/**
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* <p>A measure of how accurate the classifier results are for the test data. It is
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* derived from the <code>Precision</code> and <code>Recall</code> values. The
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* <code>F1Score</code> is the harmonic average of the two scores. The highest
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* score is 1, and the worst score is 0. </p>
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*/
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inline bool F1ScoreHasBeenSet() const { return m_f1ScoreHasBeenSet; }
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/**
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* <p>A measure of how accurate the classifier results are for the test data. It is
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* derived from the <code>Precision</code> and <code>Recall</code> values. The
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* <code>F1Score</code> is the harmonic average of the two scores. The highest
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* score is 1, and the worst score is 0. </p>
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*/
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inline void SetF1Score(double value) { m_f1ScoreHasBeenSet = true; m_f1Score = value; }
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/**
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* <p>A measure of how accurate the classifier results are for the test data. It is
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* derived from the <code>Precision</code> and <code>Recall</code> values. The
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* <code>F1Score</code> is the harmonic average of the two scores. The highest
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* score is 1, and the worst score is 0. </p>
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*/
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inline ClassifierEvaluationMetrics& WithF1Score(double value) { SetF1Score(value); return *this;}
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/**
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* <p>A measure of the usefulness of the recognizer results in the test data. High
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* precision means that the recognizer returned substantially more relevant results
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* than irrelevant ones. Unlike the Precision metric which comes from averaging the
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* precision of all available labels, this is based on the overall score of all
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* precision scores added together.</p>
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*/
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inline double GetMicroPrecision() const{ return m_microPrecision; }
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/**
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* <p>A measure of the usefulness of the recognizer results in the test data. High
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* precision means that the recognizer returned substantially more relevant results
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* than irrelevant ones. Unlike the Precision metric which comes from averaging the
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* precision of all available labels, this is based on the overall score of all
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* precision scores added together.</p>
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*/
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inline bool MicroPrecisionHasBeenSet() const { return m_microPrecisionHasBeenSet; }
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/**
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* <p>A measure of the usefulness of the recognizer results in the test data. High
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* precision means that the recognizer returned substantially more relevant results
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* than irrelevant ones. Unlike the Precision metric which comes from averaging the
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* precision of all available labels, this is based on the overall score of all
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* precision scores added together.</p>
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*/
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inline void SetMicroPrecision(double value) { m_microPrecisionHasBeenSet = true; m_microPrecision = value; }
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/**
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* <p>A measure of the usefulness of the recognizer results in the test data. High
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* precision means that the recognizer returned substantially more relevant results
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* than irrelevant ones. Unlike the Precision metric which comes from averaging the
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* precision of all available labels, this is based on the overall score of all
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* precision scores added together.</p>
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*/
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inline ClassifierEvaluationMetrics& WithMicroPrecision(double value) { SetMicroPrecision(value); return *this;}
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/**
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* <p>A measure of how complete the classifier results are for the test data. High
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* recall means that the classifier returned most of the relevant results.
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* Specifically, this indicates how many of the correct categories in the text that
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* the model can predict. It is a percentage of correct categories in the text that
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* can found. Instead of averaging the recall scores of all labels (as with
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* Recall), micro Recall is based on the overall score of all recall scores added
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* together.</p>
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*/
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inline double GetMicroRecall() const{ return m_microRecall; }
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/**
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* <p>A measure of how complete the classifier results are for the test data. High
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* recall means that the classifier returned most of the relevant results.
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* Specifically, this indicates how many of the correct categories in the text that
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* the model can predict. It is a percentage of correct categories in the text that
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* can found. Instead of averaging the recall scores of all labels (as with
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* Recall), micro Recall is based on the overall score of all recall scores added
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* together.</p>
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*/
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inline bool MicroRecallHasBeenSet() const { return m_microRecallHasBeenSet; }
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/**
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* <p>A measure of how complete the classifier results are for the test data. High
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* recall means that the classifier returned most of the relevant results.
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* Specifically, this indicates how many of the correct categories in the text that
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* the model can predict. It is a percentage of correct categories in the text that
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* can found. Instead of averaging the recall scores of all labels (as with
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* Recall), micro Recall is based on the overall score of all recall scores added
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* together.</p>
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*/
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inline void SetMicroRecall(double value) { m_microRecallHasBeenSet = true; m_microRecall = value; }
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/**
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* <p>A measure of how complete the classifier results are for the test data. High
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* recall means that the classifier returned most of the relevant results.
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* Specifically, this indicates how many of the correct categories in the text that
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* the model can predict. It is a percentage of correct categories in the text that
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* can found. Instead of averaging the recall scores of all labels (as with
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* Recall), micro Recall is based on the overall score of all recall scores added
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* together.</p>
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*/
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inline ClassifierEvaluationMetrics& WithMicroRecall(double value) { SetMicroRecall(value); return *this;}
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/**
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* <p>A measure of how accurate the classifier results are for the test data. It is
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* a combination of the <code>Micro Precision</code> and <code>Micro Recall</code>
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* values. The <code>Micro F1Score</code> is the harmonic mean of the two scores.
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* The highest score is 1, and the worst score is 0.</p>
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*/
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inline double GetMicroF1Score() const{ return m_microF1Score; }
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/**
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* <p>A measure of how accurate the classifier results are for the test data. It is
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* a combination of the <code>Micro Precision</code> and <code>Micro Recall</code>
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* values. The <code>Micro F1Score</code> is the harmonic mean of the two scores.
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* The highest score is 1, and the worst score is 0.</p>
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*/
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inline bool MicroF1ScoreHasBeenSet() const { return m_microF1ScoreHasBeenSet; }
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/**
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* <p>A measure of how accurate the classifier results are for the test data. It is
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* a combination of the <code>Micro Precision</code> and <code>Micro Recall</code>
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* values. The <code>Micro F1Score</code> is the harmonic mean of the two scores.
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* The highest score is 1, and the worst score is 0.</p>
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*/
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inline void SetMicroF1Score(double value) { m_microF1ScoreHasBeenSet = true; m_microF1Score = value; }
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/**
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* <p>A measure of how accurate the classifier results are for the test data. It is
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* a combination of the <code>Micro Precision</code> and <code>Micro Recall</code>
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* values. The <code>Micro F1Score</code> is the harmonic mean of the two scores.
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* The highest score is 1, and the worst score is 0.</p>
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*/
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inline ClassifierEvaluationMetrics& WithMicroF1Score(double value) { SetMicroF1Score(value); return *this;}
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/**
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* <p>Indicates the fraction of labels that are incorrectly predicted. Also seen as
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* the fraction of wrong labels compared to the total number of labels. Scores
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* closer to zero are better.</p>
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*/
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inline double GetHammingLoss() const{ return m_hammingLoss; }
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/**
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* <p>Indicates the fraction of labels that are incorrectly predicted. Also seen as
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* the fraction of wrong labels compared to the total number of labels. Scores
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* closer to zero are better.</p>
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*/
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inline bool HammingLossHasBeenSet() const { return m_hammingLossHasBeenSet; }
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/**
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* <p>Indicates the fraction of labels that are incorrectly predicted. Also seen as
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* the fraction of wrong labels compared to the total number of labels. Scores
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* closer to zero are better.</p>
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*/
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inline void SetHammingLoss(double value) { m_hammingLossHasBeenSet = true; m_hammingLoss = value; }
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/**
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* <p>Indicates the fraction of labels that are incorrectly predicted. Also seen as
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* the fraction of wrong labels compared to the total number of labels. Scores
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* closer to zero are better.</p>
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*/
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inline ClassifierEvaluationMetrics& WithHammingLoss(double value) { SetHammingLoss(value); return *this;}
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private:
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double m_accuracy;
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bool m_accuracyHasBeenSet;
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double m_precision;
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bool m_precisionHasBeenSet;
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double m_recall;
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bool m_recallHasBeenSet;
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double m_f1Score;
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bool m_f1ScoreHasBeenSet;
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double m_microPrecision;
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bool m_microPrecisionHasBeenSet;
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double m_microRecall;
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bool m_microRecallHasBeenSet;
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double m_microF1Score;
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bool m_microF1ScoreHasBeenSet;
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double m_hammingLoss;
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bool m_hammingLossHasBeenSet;
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};
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} // namespace Model
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} // namespace Comprehend
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} // namespace Aws
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