public class ClassifierEvaluationMetrics
extends java.lang.Object
implements java.io.Serializable
Describes the result metrics for the test data associated with an documentation classifier.
Constructor and Description 

ClassifierEvaluationMetrics() 
Modifier and Type  Method and Description 

boolean 
equals(java.lang.Object obj) 
java.lang.Double 
getAccuracy()
The fraction of the labels that were correct recognized.

java.lang.Double 
getF1Score()
A measure of how accurate the classifier results are for the test data.

java.lang.Double 
getHammingLoss()
Indicates the fraction of labels that are incorrectly predicted.

java.lang.Double 
getMicroF1Score()
A measure of how accurate the classifier results are for the test data.

java.lang.Double 
getMicroPrecision()
A measure of the usefulness of the recognizer results in the test data.

java.lang.Double 
getMicroRecall()
A measure of how complete the classifier results are for the test data.

java.lang.Double 
getPrecision()
A measure of the usefulness of the classifier results in the test data.

java.lang.Double 
getRecall()
A measure of how complete the classifier results are for the test data.

int 
hashCode() 
void 
setAccuracy(java.lang.Double accuracy)
The fraction of the labels that were correct recognized.

void 
setF1Score(java.lang.Double f1Score)
A measure of how accurate the classifier results are for the test data.

void 
setHammingLoss(java.lang.Double hammingLoss)
Indicates the fraction of labels that are incorrectly predicted.

void 
setMicroF1Score(java.lang.Double microF1Score)
A measure of how accurate the classifier results are for the test data.

void 
setMicroPrecision(java.lang.Double microPrecision)
A measure of the usefulness of the recognizer results in the test data.

void 
setMicroRecall(java.lang.Double microRecall)
A measure of how complete the classifier results are for the test data.

void 
setPrecision(java.lang.Double precision)
A measure of the usefulness of the classifier results in the test data.

void 
setRecall(java.lang.Double recall)
A measure of how complete the classifier results are for the test data.

java.lang.String 
toString()
Returns a string representation of this object; useful for testing and
debugging.

ClassifierEvaluationMetrics 
withAccuracy(java.lang.Double accuracy)
The fraction of the labels that were correct recognized.

ClassifierEvaluationMetrics 
withF1Score(java.lang.Double f1Score)
A measure of how accurate the classifier results are for the test data.

ClassifierEvaluationMetrics 
withHammingLoss(java.lang.Double hammingLoss)
Indicates the fraction of labels that are incorrectly predicted.

ClassifierEvaluationMetrics 
withMicroF1Score(java.lang.Double microF1Score)
A measure of how accurate the classifier results are for the test data.

ClassifierEvaluationMetrics 
withMicroPrecision(java.lang.Double microPrecision)
A measure of the usefulness of the recognizer results in the test data.

ClassifierEvaluationMetrics 
withMicroRecall(java.lang.Double microRecall)
A measure of how complete the classifier results are for the test data.

ClassifierEvaluationMetrics 
withPrecision(java.lang.Double precision)
A measure of the usefulness of the classifier results in the test data.

ClassifierEvaluationMetrics 
withRecall(java.lang.Double recall)
A measure of how complete the classifier results are for the test data.

public java.lang.Double getAccuracy()
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.
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.
public void setAccuracy(java.lang.Double accuracy)
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.
accuracy
 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.
public ClassifierEvaluationMetrics withAccuracy(java.lang.Double accuracy)
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.
Returns a reference to this object so that method calls can be chained together.
accuracy
 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.
public java.lang.Double getPrecision()
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.
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.
public void setPrecision(java.lang.Double precision)
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.
precision
 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.
public ClassifierEvaluationMetrics withPrecision(java.lang.Double precision)
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.
Returns a reference to this object so that method calls can be chained together.
precision
 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.
public java.lang.Double getRecall()
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.
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.
public void setRecall(java.lang.Double recall)
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.
recall
 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.
public ClassifierEvaluationMetrics withRecall(java.lang.Double recall)
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.
Returns a reference to this object so that method calls can be chained together.
recall
 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.
public java.lang.Double getF1Score()
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.
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.
public void setF1Score(java.lang.Double f1Score)
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.
f1Score

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.
public ClassifierEvaluationMetrics withF1Score(java.lang.Double f1Score)
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.
Returns a reference to this object so that method calls can be chained together.
f1Score

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.
public java.lang.Double getMicroPrecision()
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.
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.
public void setMicroPrecision(java.lang.Double microPrecision)
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.
microPrecision
 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.
public ClassifierEvaluationMetrics withMicroPrecision(java.lang.Double microPrecision)
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.
Returns a reference to this object so that method calls can be chained together.
microPrecision
 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.
public java.lang.Double getMicroRecall()
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.
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.
public void setMicroRecall(java.lang.Double microRecall)
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.
microRecall
 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.
public ClassifierEvaluationMetrics withMicroRecall(java.lang.Double microRecall)
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.
Returns a reference to this object so that method calls can be chained together.
microRecall
 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.
public java.lang.Double getMicroF1Score()
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.
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.
public void setMicroF1Score(java.lang.Double microF1Score)
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.
microF1Score

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.
public ClassifierEvaluationMetrics withMicroF1Score(java.lang.Double microF1Score)
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.
Returns a reference to this object so that method calls can be chained together.
microF1Score

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.
public java.lang.Double getHammingLoss()
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.
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.
public void setHammingLoss(java.lang.Double hammingLoss)
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.
hammingLoss
 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.
public ClassifierEvaluationMetrics withHammingLoss(java.lang.Double hammingLoss)
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.
Returns a reference to this object so that method calls can be chained together.
hammingLoss
 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.
public java.lang.String toString()
toString
in class java.lang.Object
Object.toString()
public int hashCode()
hashCode
in class java.lang.Object
public boolean equals(java.lang.Object obj)
equals
in class java.lang.Object
Copyright © 2018 Amazon Web Services, Inc. All Rights Reserved.