@Generated(value="com.amazonaws:aws-java-sdk-code-generator") public class FindMatchesParameters extends Object implements Serializable, Cloneable, StructuredPojo
The parameters to configure the find matches transform.
| Constructor and Description |
|---|
FindMatchesParameters() |
| Modifier and Type | Method and Description |
|---|---|
FindMatchesParameters |
clone() |
boolean |
equals(Object obj) |
Double |
getAccuracyCostTradeoff()
The value that is selected when tuning your transform for a balance between accuracy and cost.
|
Boolean |
getEnforceProvidedLabels()
The value to switch on or off to force the output to match the provided labels from users.
|
Double |
getPrecisionRecallTradeoff()
The value selected when tuning your transform for a balance between precision and recall.
|
String |
getPrimaryKeyColumnName()
The name of a column that uniquely identifies rows in the source table.
|
int |
hashCode() |
Boolean |
isEnforceProvidedLabels()
The value to switch on or off to force the output to match the provided labels from users.
|
void |
marshall(ProtocolMarshaller protocolMarshaller)
Marshalls this structured data using the given
ProtocolMarshaller. |
void |
setAccuracyCostTradeoff(Double accuracyCostTradeoff)
The value that is selected when tuning your transform for a balance between accuracy and cost.
|
void |
setEnforceProvidedLabels(Boolean enforceProvidedLabels)
The value to switch on or off to force the output to match the provided labels from users.
|
void |
setPrecisionRecallTradeoff(Double precisionRecallTradeoff)
The value selected when tuning your transform for a balance between precision and recall.
|
void |
setPrimaryKeyColumnName(String primaryKeyColumnName)
The name of a column that uniquely identifies rows in the source table.
|
String |
toString()
Returns a string representation of this object.
|
FindMatchesParameters |
withAccuracyCostTradeoff(Double accuracyCostTradeoff)
The value that is selected when tuning your transform for a balance between accuracy and cost.
|
FindMatchesParameters |
withEnforceProvidedLabels(Boolean enforceProvidedLabels)
The value to switch on or off to force the output to match the provided labels from users.
|
FindMatchesParameters |
withPrecisionRecallTradeoff(Double precisionRecallTradeoff)
The value selected when tuning your transform for a balance between precision and recall.
|
FindMatchesParameters |
withPrimaryKeyColumnName(String primaryKeyColumnName)
The name of a column that uniquely identifies rows in the source table.
|
public void setPrimaryKeyColumnName(String primaryKeyColumnName)
The name of a column that uniquely identifies rows in the source table. Used to help identify matching records.
primaryKeyColumnName - The name of a column that uniquely identifies rows in the source table. Used to help identify matching
records.public String getPrimaryKeyColumnName()
The name of a column that uniquely identifies rows in the source table. Used to help identify matching records.
public FindMatchesParameters withPrimaryKeyColumnName(String primaryKeyColumnName)
The name of a column that uniquely identifies rows in the source table. Used to help identify matching records.
primaryKeyColumnName - The name of a column that uniquely identifies rows in the source table. Used to help identify matching
records.public void setPrecisionRecallTradeoff(Double precisionRecallTradeoff)
The value selected when tuning your transform for a balance between precision and recall. A value of 0.5 means no preference; a value of 1.0 means a bias purely for precision, and a value of 0.0 means a bias for recall. Because this is a tradeoff, choosing values close to 1.0 means very low recall, and choosing values close to 0.0 results in very low precision.
The precision metric indicates how often your model is correct when it predicts a match.
The recall metric indicates that for an actual match, how often your model predicts the match.
precisionRecallTradeoff - The value selected when tuning your transform for a balance between precision and recall. A value of 0.5
means no preference; a value of 1.0 means a bias purely for precision, and a value of 0.0 means a bias for
recall. Because this is a tradeoff, choosing values close to 1.0 means very low recall, and choosing
values close to 0.0 results in very low precision.
The precision metric indicates how often your model is correct when it predicts a match.
The recall metric indicates that for an actual match, how often your model predicts the match.
public Double getPrecisionRecallTradeoff()
The value selected when tuning your transform for a balance between precision and recall. A value of 0.5 means no preference; a value of 1.0 means a bias purely for precision, and a value of 0.0 means a bias for recall. Because this is a tradeoff, choosing values close to 1.0 means very low recall, and choosing values close to 0.0 results in very low precision.
The precision metric indicates how often your model is correct when it predicts a match.
The recall metric indicates that for an actual match, how often your model predicts the match.
The precision metric indicates how often your model is correct when it predicts a match.
The recall metric indicates that for an actual match, how often your model predicts the match.
public FindMatchesParameters withPrecisionRecallTradeoff(Double precisionRecallTradeoff)
The value selected when tuning your transform for a balance between precision and recall. A value of 0.5 means no preference; a value of 1.0 means a bias purely for precision, and a value of 0.0 means a bias for recall. Because this is a tradeoff, choosing values close to 1.0 means very low recall, and choosing values close to 0.0 results in very low precision.
The precision metric indicates how often your model is correct when it predicts a match.
The recall metric indicates that for an actual match, how often your model predicts the match.
precisionRecallTradeoff - The value selected when tuning your transform for a balance between precision and recall. A value of 0.5
means no preference; a value of 1.0 means a bias purely for precision, and a value of 0.0 means a bias for
recall. Because this is a tradeoff, choosing values close to 1.0 means very low recall, and choosing
values close to 0.0 results in very low precision.
The precision metric indicates how often your model is correct when it predicts a match.
The recall metric indicates that for an actual match, how often your model predicts the match.
public void setAccuracyCostTradeoff(Double accuracyCostTradeoff)
The value that is selected when tuning your transform for a balance between accuracy and cost. A value of 0.5
means that the system balances accuracy and cost concerns. A value of 1.0 means a bias purely for accuracy, which
typically results in a higher cost, sometimes substantially higher. A value of 0.0 means a bias purely for cost,
which results in a less accurate FindMatches transform, sometimes with unacceptable accuracy.
Accuracy measures how well the transform finds true positives and true negatives. Increasing accuracy requires more machine resources and cost. But it also results in increased recall.
Cost measures how many compute resources, and thus money, are consumed to run the transform.
accuracyCostTradeoff - The value that is selected when tuning your transform for a balance between accuracy and cost. A value of
0.5 means that the system balances accuracy and cost concerns. A value of 1.0 means a bias purely for
accuracy, which typically results in a higher cost, sometimes substantially higher. A value of 0.0 means a
bias purely for cost, which results in a less accurate FindMatches transform, sometimes with
unacceptable accuracy.
Accuracy measures how well the transform finds true positives and true negatives. Increasing accuracy requires more machine resources and cost. But it also results in increased recall.
Cost measures how many compute resources, and thus money, are consumed to run the transform.
public Double getAccuracyCostTradeoff()
The value that is selected when tuning your transform for a balance between accuracy and cost. A value of 0.5
means that the system balances accuracy and cost concerns. A value of 1.0 means a bias purely for accuracy, which
typically results in a higher cost, sometimes substantially higher. A value of 0.0 means a bias purely for cost,
which results in a less accurate FindMatches transform, sometimes with unacceptable accuracy.
Accuracy measures how well the transform finds true positives and true negatives. Increasing accuracy requires more machine resources and cost. But it also results in increased recall.
Cost measures how many compute resources, and thus money, are consumed to run the transform.
FindMatches transform, sometimes
with unacceptable accuracy.
Accuracy measures how well the transform finds true positives and true negatives. Increasing accuracy requires more machine resources and cost. But it also results in increased recall.
Cost measures how many compute resources, and thus money, are consumed to run the transform.
public FindMatchesParameters withAccuracyCostTradeoff(Double accuracyCostTradeoff)
The value that is selected when tuning your transform for a balance between accuracy and cost. A value of 0.5
means that the system balances accuracy and cost concerns. A value of 1.0 means a bias purely for accuracy, which
typically results in a higher cost, sometimes substantially higher. A value of 0.0 means a bias purely for cost,
which results in a less accurate FindMatches transform, sometimes with unacceptable accuracy.
Accuracy measures how well the transform finds true positives and true negatives. Increasing accuracy requires more machine resources and cost. But it also results in increased recall.
Cost measures how many compute resources, and thus money, are consumed to run the transform.
accuracyCostTradeoff - The value that is selected when tuning your transform for a balance between accuracy and cost. A value of
0.5 means that the system balances accuracy and cost concerns. A value of 1.0 means a bias purely for
accuracy, which typically results in a higher cost, sometimes substantially higher. A value of 0.0 means a
bias purely for cost, which results in a less accurate FindMatches transform, sometimes with
unacceptable accuracy.
Accuracy measures how well the transform finds true positives and true negatives. Increasing accuracy requires more machine resources and cost. But it also results in increased recall.
Cost measures how many compute resources, and thus money, are consumed to run the transform.
public void setEnforceProvidedLabels(Boolean enforceProvidedLabels)
The value to switch on or off to force the output to match the provided labels from users. If the value is
True, the find matches transform forces the output to match the provided labels. The
results override the normal conflation results. If the value is False, the find matches
transform does not ensure all the labels provided are respected, and the results rely on the trained model.
Note that setting this value to true may increase the conflation execution time.
enforceProvidedLabels - The value to switch on or off to force the output to match the provided labels from users. If the value is
True, the find matches transform forces the output to match the provided labels.
The results override the normal conflation results. If the value is False, the
find matches transform does not ensure all the labels provided are respected, and the results
rely on the trained model.
Note that setting this value to true may increase the conflation execution time.
public Boolean getEnforceProvidedLabels()
The value to switch on or off to force the output to match the provided labels from users. If the value is
True, the find matches transform forces the output to match the provided labels. The
results override the normal conflation results. If the value is False, the find matches
transform does not ensure all the labels provided are respected, and the results rely on the trained model.
Note that setting this value to true may increase the conflation execution time.
True, the find matches transform forces the output to match the provided
labels. The results override the normal conflation results. If the value is False, the
find matches transform does not ensure all the labels provided are respected, and the
results rely on the trained model.
Note that setting this value to true may increase the conflation execution time.
public FindMatchesParameters withEnforceProvidedLabels(Boolean enforceProvidedLabels)
The value to switch on or off to force the output to match the provided labels from users. If the value is
True, the find matches transform forces the output to match the provided labels. The
results override the normal conflation results. If the value is False, the find matches
transform does not ensure all the labels provided are respected, and the results rely on the trained model.
Note that setting this value to true may increase the conflation execution time.
enforceProvidedLabels - The value to switch on or off to force the output to match the provided labels from users. If the value is
True, the find matches transform forces the output to match the provided labels.
The results override the normal conflation results. If the value is False, the
find matches transform does not ensure all the labels provided are respected, and the results
rely on the trained model.
Note that setting this value to true may increase the conflation execution time.
public Boolean isEnforceProvidedLabels()
The value to switch on or off to force the output to match the provided labels from users. If the value is
True, the find matches transform forces the output to match the provided labels. The
results override the normal conflation results. If the value is False, the find matches
transform does not ensure all the labels provided are respected, and the results rely on the trained model.
Note that setting this value to true may increase the conflation execution time.
True, the find matches transform forces the output to match the provided
labels. The results override the normal conflation results. If the value is False, the
find matches transform does not ensure all the labels provided are respected, and the
results rely on the trained model.
Note that setting this value to true may increase the conflation execution time.
public String toString()
toString in class ObjectObject.toString()public FindMatchesParameters clone()
public void marshall(ProtocolMarshaller protocolMarshaller)
StructuredPojoProtocolMarshaller.marshall in interface StructuredPojoprotocolMarshaller - Implementation of ProtocolMarshaller used to marshall this object's data.