ClassifierEvaluationMetrics (AWS SDK for Java

java.lang.Object
- com.amazonaws.services.comprehend.model.ClassifierEvaluationMetrics

All Implemented Interfaces:

StructuredPojo, Serializable, Cloneable
```
@Generated(value="com.amazonaws:aws-java-sdk-code-generator")
public class ClassifierEvaluationMetrics
extends Object
implements Serializable, Cloneable, StructuredPojo
```
Describes the result metrics for the test data associated with an documentation classifier.

See Also:

AWS API Documentation, Serialized Form

Constructor Summary

Constructors
Constructor and Description

ClassifierEvaluationMetrics()

Constructors
Constructor and Description
`ClassifierEvaluationMetrics()`

Method Summary

All Methods Instance Methods Concrete Methods
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.

Methods inherited from class java.lang.Object
getClass, notify, notifyAll, wait, wait, wait

- Constructor Detail
  - ClassifierEvaluationMetrics
```
public ClassifierEvaluationMetrics()
```
- Method Detail
  - setAccuracy
```
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.
    
    Parameters:
    
    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.
  - getAccuracy
```
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.
    
    Returns:
    
    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.
  - withAccuracy
```
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.
    
    Parameters:
    
    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:
    
    Returns a reference to this object so that method calls can be chained together.
  - setPrecision
```
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.
    
    Parameters:
    
    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.
  - getPrecision
```
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.
    
    Returns:
    
    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.
  - withPrecision
```
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.
    
    Parameters:
    
    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:
    
    Returns a reference to this object so that method calls can be chained together.
  - setRecall
```
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.
    
    Parameters:
    
    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.
  - getRecall
```
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.
    
    Returns:
    
    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.
  - withRecall
```
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.
    
    Parameters:
    
    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:
    
    Returns a reference to this object so that method calls can be chained together.
  - setF1Score
```
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.
    
    Parameters:
    
    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.
  - getF1Score
```
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.
    
    Returns:
    
    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.
  - withF1Score
```
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.
    
    Parameters:
    
    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:
    
    Returns a reference to this object so that method calls can be chained together.
  - setMicroPrecision
```
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.
    
    Parameters:
    
    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.
  - getMicroPrecision
```
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.
    
    Returns:
    
    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.
  - withMicroPrecision
```
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.
    
    Parameters:
    
    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:
    
    Returns a reference to this object so that method calls can be chained together.
  - setMicroRecall
```
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.
    
    Parameters:
    
    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.
  - getMicroRecall
```
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.
    
    Returns:
    
    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.
  - withMicroRecall
```
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.
    
    Parameters:
    
    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:
    
    Returns a reference to this object so that method calls can be chained together.
  - setMicroF1Score
```
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.
    
    Parameters:
    
    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.
  - getMicroF1Score
```
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.
    
    Returns:
    
    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.
  - withMicroF1Score
```
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.
    
    Parameters:
    
    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:
    
    Returns a reference to this object so that method calls can be chained together.
  - setHammingLoss
```
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.
    
    Parameters:
    
    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.
  - getHammingLoss
```
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.
    
    Returns:
    
    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.
  - withHammingLoss
```
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.
    
    Parameters:
    
    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:
    
    Returns a reference to this object so that method calls can be chained together.
  - toString
```
public String toString()
```
    Returns a string representation of this object. This is useful for testing and debugging. Sensitive data will be redacted from this string using a placeholder value.
    
    Overrides:
    
    toString in class Object
    
    Returns:
    
    A string representation of this object.
    
    See Also:
    
    Object.toString()
  - equals
```
public boolean equals(Object obj)
```
    Overrides:
    
    equals in class Object
  - hashCode
```
public int hashCode()
```
    Overrides:
    
    hashCode in class Object
  - clone
```
public ClassifierEvaluationMetrics clone()
```
    Overrides:
    
    clone in class Object
  - marshall
```
public void marshall(ProtocolMarshaller protocolMarshaller)
```
    Description copied from interface: StructuredPojo
    
    Marshalls this structured data using the given ProtocolMarshaller.
    
    Specified by:
    
    marshall in interface StructuredPojo
    
    Parameters:
    
    protocolMarshaller - Implementation of ProtocolMarshaller used to marshall this object's data.

AWS SDK for Java 1.x API Reference - 1.12.780

Class ClassifierEvaluationMetrics

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Constructor Detail

ClassifierEvaluationMetrics

Method Detail

setAccuracy

getAccuracy

withAccuracy

setPrecision

getPrecision

withPrecision

setRecall

getRecall

withRecall

setF1Score

getF1Score

withF1Score

setMicroPrecision

getMicroPrecision

withMicroPrecision

setMicroRecall

getMicroRecall

withMicroRecall

setMicroF1Score

getMicroF1Score

withMicroF1Score

setHammingLoss

getHammingLoss

withHammingLoss

toString

equals

hashCode

clone

marshall