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`	`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`	`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`	`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.
  - 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.

Did this page help you?

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

toString

equals

hashCode

clone

marshall