@Generated(value="com.amazonaws:awsjavasdkcodegenerator") public class ClassifierEvaluationMetrics extends Object implements Serializable, Cloneable, StructuredPojo
Describes the result metrics for the test data associated with an documentation classifier.
Constructor and Description 

ClassifierEvaluationMetrics() 
Modifier and Type  Method and Description 

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

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

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

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

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

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

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

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

int 
hashCode() 
void 
marshall(ProtocolMarshaller protocolMarshaller)
Marshalls this structured data using the given
ProtocolMarshaller . 
void 
setAccuracy(Double accuracy)
The fraction of the labels that were correct recognized.

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

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

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

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

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

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

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

String 
toString()
Returns a string representation of this object.

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

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

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

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

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

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

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

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

public void setAccuracy(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 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.
public ClassifierEvaluationMetrics withAccuracy(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 void setPrecision(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 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.
public ClassifierEvaluationMetrics withPrecision(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 void setRecall(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 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.
public ClassifierEvaluationMetrics withRecall(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 void setF1Score(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 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.
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(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 void setMicroPrecision(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 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.
public ClassifierEvaluationMetrics withMicroPrecision(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 void setMicroRecall(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 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.
public ClassifierEvaluationMetrics withMicroRecall(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 void setMicroF1Score(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 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.
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(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 void setHammingLoss(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 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.
public ClassifierEvaluationMetrics withHammingLoss(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 String toString()
toString
in class Object
Object.toString()
public ClassifierEvaluationMetrics clone()
public void marshall(ProtocolMarshaller protocolMarshaller)
StructuredPojo
ProtocolMarshaller
.marshall
in interface StructuredPojo
protocolMarshaller
 Implementation of ProtocolMarshaller
used to marshall this object's data.