FindMatchesMetrics (AWS SDK for Java

java.lang.Object
- com.amazonaws.services.glue.model.FindMatchesMetrics

All Implemented Interfaces:

StructuredPojo, Serializable, Cloneable
```
@Generated(value="com.amazonaws:aws-java-sdk-code-generator")
public class FindMatchesMetrics
extends Object
implements Serializable, Cloneable, StructuredPojo
```
The evaluation metrics for the find matches algorithm. The quality of your machine learning transform is measured by getting your transform to predict some matches and comparing the results to known matches from the same dataset. The quality metrics are based on a subset of your data, so they are not precise.

See Also:

AWS API Documentation, Serialized Form

Constructor Summary

Constructors
Constructor and Description

FindMatchesMetrics()

Constructors
Constructor and Description
`FindMatchesMetrics()`

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`FindMatchesMetrics`	`clone()`
`boolean`	`equals(Object obj)`
`Double`	`getAreaUnderPRCurve()` The area under the precision/recall curve (AUPRC) is a single number measuring the overall quality of the transform, that is independent of the choice made for precision vs.
`List<ColumnImportance>`	`getColumnImportances()` A list of `ColumnImportance` structures containing column importance metrics, sorted in order of descending importance.
`ConfusionMatrix`	`getConfusionMatrix()` The confusion matrix shows you what your transform is predicting accurately and what types of errors it is making.
`Double`	`getF1()` The maximum F1 metric indicates the transform's accuracy between 0 and 1, where 1 is the best accuracy.
`Double`	`getPrecision()` The precision metric indicates when often your transform is correct when it predicts a match.
`Double`	`getRecall()` The recall metric indicates that for an actual match, how often your transform predicts the match.
`int`	`hashCode()`
`void`	`marshall(ProtocolMarshaller protocolMarshaller)` Marshalls this structured data using the given `ProtocolMarshaller`.
`void`	`setAreaUnderPRCurve(Double areaUnderPRCurve)` The area under the precision/recall curve (AUPRC) is a single number measuring the overall quality of the transform, that is independent of the choice made for precision vs.
`void`	`setColumnImportances(Collection<ColumnImportance> columnImportances)` A list of `ColumnImportance` structures containing column importance metrics, sorted in order of descending importance.
`void`	`setConfusionMatrix(ConfusionMatrix confusionMatrix)` The confusion matrix shows you what your transform is predicting accurately and what types of errors it is making.
`void`	`setF1(Double f1)` The maximum F1 metric indicates the transform's accuracy between 0 and 1, where 1 is the best accuracy.
`void`	`setPrecision(Double precision)` The precision metric indicates when often your transform is correct when it predicts a match.
`void`	`setRecall(Double recall)` The recall metric indicates that for an actual match, how often your transform predicts the match.
`String`	`toString()` Returns a string representation of this object.
`FindMatchesMetrics`	`withAreaUnderPRCurve(Double areaUnderPRCurve)` The area under the precision/recall curve (AUPRC) is a single number measuring the overall quality of the transform, that is independent of the choice made for precision vs.
`FindMatchesMetrics`	`withColumnImportances(Collection<ColumnImportance> columnImportances)` A list of `ColumnImportance` structures containing column importance metrics, sorted in order of descending importance.
`FindMatchesMetrics`	`withColumnImportances(ColumnImportance... columnImportances)` A list of `ColumnImportance` structures containing column importance metrics, sorted in order of descending importance.
`FindMatchesMetrics`	`withConfusionMatrix(ConfusionMatrix confusionMatrix)` The confusion matrix shows you what your transform is predicting accurately and what types of errors it is making.
`FindMatchesMetrics`	`withF1(Double f1)` The maximum F1 metric indicates the transform's accuracy between 0 and 1, where 1 is the best accuracy.
`FindMatchesMetrics`	`withPrecision(Double precision)` The precision metric indicates when often your transform is correct when it predicts a match.
`FindMatchesMetrics`	`withRecall(Double recall)` The recall metric indicates that for an actual match, how often your transform predicts the match.

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

- Constructor Detail
  - FindMatchesMetrics
```
public FindMatchesMetrics()
```
- Method Detail
  - setAreaUnderPRCurve
```
public void setAreaUnderPRCurve(Double areaUnderPRCurve)
```
    The area under the precision/recall curve (AUPRC) is a single number measuring the overall quality of the transform, that is independent of the choice made for precision vs. recall. Higher values indicate that you have a more attractive precision vs. recall tradeoff.
    
    For more information, see Precision and recall in Wikipedia.
    
    Parameters:
    
    areaUnderPRCurve - The area under the precision/recall curve (AUPRC) is a single number measuring the overall quality of the transform, that is independent of the choice made for precision vs. recall. Higher values indicate that you have a more attractive precision vs. recall tradeoff.
    
    For more information, see Precision and recall in Wikipedia.
  - getAreaUnderPRCurve
```
public Double getAreaUnderPRCurve()
```
    The area under the precision/recall curve (AUPRC) is a single number measuring the overall quality of the transform, that is independent of the choice made for precision vs. recall. Higher values indicate that you have a more attractive precision vs. recall tradeoff.
    
    For more information, see Precision and recall in Wikipedia.
    
    Returns:
    
    The area under the precision/recall curve (AUPRC) is a single number measuring the overall quality of the transform, that is independent of the choice made for precision vs. recall. Higher values indicate that you have a more attractive precision vs. recall tradeoff.
    
    For more information, see Precision and recall in Wikipedia.
  - withAreaUnderPRCurve
```
public FindMatchesMetrics withAreaUnderPRCurve(Double areaUnderPRCurve)
```
    The area under the precision/recall curve (AUPRC) is a single number measuring the overall quality of the transform, that is independent of the choice made for precision vs. recall. Higher values indicate that you have a more attractive precision vs. recall tradeoff.
    
    For more information, see Precision and recall in Wikipedia.
    
    Parameters:
    
    areaUnderPRCurve - The area under the precision/recall curve (AUPRC) is a single number measuring the overall quality of the transform, that is independent of the choice made for precision vs. recall. Higher values indicate that you have a more attractive precision vs. recall tradeoff.
    
    For more information, see Precision and recall in Wikipedia.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setPrecision
```
public void setPrecision(Double precision)
```
    The precision metric indicates when often your transform is correct when it predicts a match. Specifically, it measures how well the transform finds true positives from the total true positives possible.
    
    For more information, see Precision and recall in Wikipedia.
    
    Parameters:
    
    precision - The precision metric indicates when often your transform is correct when it predicts a match. Specifically, it measures how well the transform finds true positives from the total true positives possible.
    
    For more information, see Precision and recall in Wikipedia.
  - getPrecision
```
public Double getPrecision()
```
    The precision metric indicates when often your transform is correct when it predicts a match. Specifically, it measures how well the transform finds true positives from the total true positives possible.
    
    For more information, see Precision and recall in Wikipedia.
    
    Returns:
    
    The precision metric indicates when often your transform is correct when it predicts a match. Specifically, it measures how well the transform finds true positives from the total true positives possible.
    
    For more information, see Precision and recall in Wikipedia.
  - withPrecision
```
public FindMatchesMetrics withPrecision(Double precision)
```
    The precision metric indicates when often your transform is correct when it predicts a match. Specifically, it measures how well the transform finds true positives from the total true positives possible.
    
    For more information, see Precision and recall in Wikipedia.
    
    Parameters:
    
    precision - The precision metric indicates when often your transform is correct when it predicts a match. Specifically, it measures how well the transform finds true positives from the total true positives possible.
    
    For more information, see Precision and recall in Wikipedia.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setRecall
```
public void setRecall(Double recall)
```
    The recall metric indicates that for an actual match, how often your transform predicts the match. Specifically, it measures how well the transform finds true positives from the total records in the source data.
    
    For more information, see Precision and recall in Wikipedia.
    
    Parameters:
    
    recall - The recall metric indicates that for an actual match, how often your transform predicts the match. Specifically, it measures how well the transform finds true positives from the total records in the source data.
    
    For more information, see Precision and recall in Wikipedia.
  - getRecall
```
public Double getRecall()
```
    The recall metric indicates that for an actual match, how often your transform predicts the match. Specifically, it measures how well the transform finds true positives from the total records in the source data.
    
    For more information, see Precision and recall in Wikipedia.
    
    Returns:
    
    The recall metric indicates that for an actual match, how often your transform predicts the match. Specifically, it measures how well the transform finds true positives from the total records in the source data.
    
    For more information, see Precision and recall in Wikipedia.
  - withRecall
```
public FindMatchesMetrics withRecall(Double recall)
```
    The recall metric indicates that for an actual match, how often your transform predicts the match. Specifically, it measures how well the transform finds true positives from the total records in the source data.
    
    For more information, see Precision and recall in Wikipedia.
    
    Parameters:
    
    recall - The recall metric indicates that for an actual match, how often your transform predicts the match. Specifically, it measures how well the transform finds true positives from the total records in the source data.
    
    For more information, see Precision and recall in Wikipedia.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setF1
```
public void setF1(Double f1)
```
    The maximum F1 metric indicates the transform's accuracy between 0 and 1, where 1 is the best accuracy.
    
    For more information, see F1 score in Wikipedia.
    
    Parameters:
    
    f1 - The maximum F1 metric indicates the transform's accuracy between 0 and 1, where 1 is the best accuracy.
    
    For more information, see F1 score in Wikipedia.
  - getF1
```
public Double getF1()
```
    The maximum F1 metric indicates the transform's accuracy between 0 and 1, where 1 is the best accuracy.
    
    For more information, see F1 score in Wikipedia.
    
    Returns:
    
    The maximum F1 metric indicates the transform's accuracy between 0 and 1, where 1 is the best accuracy.
    
    For more information, see F1 score in Wikipedia.
  - withF1
```
public FindMatchesMetrics withF1(Double f1)
```
    The maximum F1 metric indicates the transform's accuracy between 0 and 1, where 1 is the best accuracy.
    
    For more information, see F1 score in Wikipedia.
    
    Parameters:
    
    f1 - The maximum F1 metric indicates the transform's accuracy between 0 and 1, where 1 is the best accuracy.
    
    For more information, see F1 score in Wikipedia.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setConfusionMatrix
```
public void setConfusionMatrix(ConfusionMatrix confusionMatrix)
```
    The confusion matrix shows you what your transform is predicting accurately and what types of errors it is making.
    
    For more information, see Confusion matrix in Wikipedia.
    
    Parameters:
    
    confusionMatrix - The confusion matrix shows you what your transform is predicting accurately and what types of errors it is making.
    
    For more information, see Confusion matrix in Wikipedia.
  - getConfusionMatrix
```
public ConfusionMatrix getConfusionMatrix()
```
    The confusion matrix shows you what your transform is predicting accurately and what types of errors it is making.
    
    For more information, see Confusion matrix in Wikipedia.
    
    Returns:
    
    The confusion matrix shows you what your transform is predicting accurately and what types of errors it is making.
    
    For more information, see Confusion matrix in Wikipedia.
  - withConfusionMatrix
```
public FindMatchesMetrics withConfusionMatrix(ConfusionMatrix confusionMatrix)
```
    The confusion matrix shows you what your transform is predicting accurately and what types of errors it is making.
    
    For more information, see Confusion matrix in Wikipedia.
    
    Parameters:
    
    confusionMatrix - The confusion matrix shows you what your transform is predicting accurately and what types of errors it is making.
    
    For more information, see Confusion matrix in Wikipedia.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - getColumnImportances
```
public List<ColumnImportance> getColumnImportances()
```
    A list of ColumnImportance structures containing column importance metrics, sorted in order of descending importance.
    
    Returns:
    
    A list of ColumnImportance structures containing column importance metrics, sorted in order of descending importance.
  - setColumnImportances
```
public void setColumnImportances(Collection<ColumnImportance> columnImportances)
```
    A list of ColumnImportance structures containing column importance metrics, sorted in order of descending importance.
    
    Parameters:
    
    columnImportances - A list of ColumnImportance structures containing column importance metrics, sorted in order of descending importance.
  - withColumnImportances
```
public FindMatchesMetrics withColumnImportances(ColumnImportance... columnImportances)
```
    A list of ColumnImportance structures containing column importance metrics, sorted in order of descending importance.
    
    NOTE: This method appends the values to the existing list (if any). Use setColumnImportances(java.util.Collection) or withColumnImportances(java.util.Collection) if you want to override the existing values.
    
    Parameters:
    
    columnImportances - A list of ColumnImportance structures containing column importance metrics, sorted in order of descending importance.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - withColumnImportances
```
public FindMatchesMetrics withColumnImportances(Collection<ColumnImportance> columnImportances)
```
    A list of ColumnImportance structures containing column importance metrics, sorted in order of descending importance.
    
    Parameters:
    
    columnImportances - A list of ColumnImportance structures containing column importance metrics, sorted in order of descending importance.
    
    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 FindMatchesMetrics 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 - 1.12.773

Class FindMatchesMetrics

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Constructor Detail

FindMatchesMetrics

Method Detail

setAreaUnderPRCurve

getAreaUnderPRCurve

withAreaUnderPRCurve

setPrecision

getPrecision

withPrecision

setRecall

getRecall

withRecall

setF1

getF1

withF1

setConfusionMatrix

getConfusionMatrix

withConfusionMatrix

getColumnImportances

setColumnImportances

withColumnImportances

withColumnImportances

toString

equals

hashCode

clone

marshall