public class PerformanceMetrics extends Object implements Serializable, Cloneable
 Measurements of how well the MLModel performed on known
 observations. One of the following metrics is returned, based on the type of
 the MLModel:
 
 BinaryAUC: The binary MLModel uses the Area Under the Curve
 (AUC) technique to measure performance.
 
 RegressionRMSE: The regression MLModel uses the Root Mean Square
 Error (RMSE) technique to measure performance. RMSE measures the difference
 between predicted and actual values for a single variable.
 
 MulticlassAvgFScore: The multiclass MLModel uses the F1 score
 technique to measure performance.
 
For more information about performance metrics, please see the Amazon Machine Learning Developer Guide.
| Constructor and Description | 
|---|
| PerformanceMetrics() | 
| Modifier and Type | Method and Description | 
|---|---|
| PerformanceMetrics | addPropertiesEntry(String key,
                  String value) | 
| PerformanceMetrics | clearPropertiesEntries()Removes all the entries added into Properties. | 
| PerformanceMetrics | clone() | 
| boolean | equals(Object obj) | 
| Map<String,String> | getProperties() | 
| int | hashCode() | 
| void | setProperties(Map<String,String> properties) | 
| String | toString()Returns a string representation of this object; useful for testing and
 debugging. | 
| PerformanceMetrics | withProperties(Map<String,String> properties) | 
public PerformanceMetrics withProperties(Map<String,String> properties)
properties - public PerformanceMetrics addPropertiesEntry(String key, String value)
public PerformanceMetrics clearPropertiesEntries()
public String toString()
toString in class ObjectObject.toString()public PerformanceMetrics clone()
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