AutoMLJobObjective (AWS SDK for Java

java.lang.Object
- com.amazonaws.services.sagemaker.model.AutoMLJobObjective

All Implemented Interfaces:

StructuredPojo, Serializable, Cloneable
```
@Generated(value="com.amazonaws:aws-java-sdk-code-generator")
public class AutoMLJobObjective
extends Object
implements Serializable, Cloneable, StructuredPojo
```
Specifies a metric to minimize or maximize as the objective of a job.

See Also:

AWS API Documentation, Serialized Form

Constructor Summary

Constructors
Constructor and Description

AutoMLJobObjective()

Constructors
Constructor and Description
`AutoMLJobObjective()`

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`AutoMLJobObjective`	`clone()`
`boolean`	`equals(Object obj)`
`String`	`getMetricName()` The name of the objective metric used to measure the predictive quality of a machine learning system.
`int`	`hashCode()`
`void`	`marshall(ProtocolMarshaller protocolMarshaller)` Marshalls this structured data using the given `ProtocolMarshaller`.
`void`	`setMetricName(String metricName)` The name of the objective metric used to measure the predictive quality of a machine learning system.
`String`	`toString()` Returns a string representation of this object.
`AutoMLJobObjective`	`withMetricName(AutoMLMetricEnum metricName)` The name of the objective metric used to measure the predictive quality of a machine learning system.
`AutoMLJobObjective`	`withMetricName(String metricName)` The name of the objective metric used to measure the predictive quality of a machine learning system.

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

- Constructor Detail
  - AutoMLJobObjective
```
public AutoMLJobObjective()
```
- Method Detail
  - setMetricName
```
public void setMetricName(String metricName)
```
    The name of the objective metric used to measure the predictive quality of a machine learning system. During training, the model's parameters are updated iteratively to optimize its performance based on the feedback provided by the objective metric when evaluating the model on the validation dataset.
    
    For the list of all available metrics supported by Autopilot, see Autopilot metrics.
    
    If you do not specify a metric explicitly, the default behavior is to automatically use:
    - For tabular problem types:
      
      Regression: MSE.
      
      Binary classification: F1.
      
      Multiclass classification: Accuracy.
    - For image or text classification problem types: Accuracy
    - For time-series forecasting problem types: AverageWeightedQuantileLoss
    Parameters:
    metricName - The name of the objective metric used to measure the predictive quality of a machine learning system. During training, the model's parameters are updated iteratively to optimize its performance based on the feedback provided by the objective metric when evaluating the model on the validation dataset.
    
    For the list of all available metrics supported by Autopilot, see Autopilot metrics.
    
    If you do not specify a metric explicitly, the default behavior is to automatically use:
    
    For tabular problem types:
    
    Regression: MSE.
    
    Binary classification: F1.
    
    Multiclass classification: Accuracy.
    
    For image or text classification problem types: Accuracy
    
    For time-series forecasting problem types: AverageWeightedQuantileLoss
    See Also:
    
    AutoMLMetricEnum
  - getMetricName
```
public String getMetricName()
```
    The name of the objective metric used to measure the predictive quality of a machine learning system. During training, the model's parameters are updated iteratively to optimize its performance based on the feedback provided by the objective metric when evaluating the model on the validation dataset.
    
    For the list of all available metrics supported by Autopilot, see Autopilot metrics.
    
    If you do not specify a metric explicitly, the default behavior is to automatically use:
    - For tabular problem types:
      
      Regression: MSE.
      
      Binary classification: F1.
      
      Multiclass classification: Accuracy.
    - For image or text classification problem types: Accuracy
    - For time-series forecasting problem types: AverageWeightedQuantileLoss
    Returns:
    The name of the objective metric used to measure the predictive quality of a machine learning system. During training, the model's parameters are updated iteratively to optimize its performance based on the feedback provided by the objective metric when evaluating the model on the validation dataset.
    
    For the list of all available metrics supported by Autopilot, see Autopilot metrics.
    
    If you do not specify a metric explicitly, the default behavior is to automatically use:
    
    For tabular problem types:
    
    Regression: MSE.
    
    Binary classification: F1.
    
    Multiclass classification: Accuracy.
    
    For image or text classification problem types: Accuracy
    
    For time-series forecasting problem types: AverageWeightedQuantileLoss
    See Also:
    
    AutoMLMetricEnum
  - withMetricName
```
public AutoMLJobObjective withMetricName(String metricName)
```
    The name of the objective metric used to measure the predictive quality of a machine learning system. During training, the model's parameters are updated iteratively to optimize its performance based on the feedback provided by the objective metric when evaluating the model on the validation dataset.
    
    For the list of all available metrics supported by Autopilot, see Autopilot metrics.
    
    If you do not specify a metric explicitly, the default behavior is to automatically use:
    - For tabular problem types:
      
      Regression: MSE.
      
      Binary classification: F1.
      
      Multiclass classification: Accuracy.
    - For image or text classification problem types: Accuracy
    - For time-series forecasting problem types: AverageWeightedQuantileLoss
    Parameters:
    metricName - The name of the objective metric used to measure the predictive quality of a machine learning system. During training, the model's parameters are updated iteratively to optimize its performance based on the feedback provided by the objective metric when evaluating the model on the validation dataset.
    
    For the list of all available metrics supported by Autopilot, see Autopilot metrics.
    
    If you do not specify a metric explicitly, the default behavior is to automatically use:
    
    For tabular problem types:
    
    Regression: MSE.
    
    Binary classification: F1.
    
    Multiclass classification: Accuracy.
    
    For image or text classification problem types: Accuracy
    
    For time-series forecasting problem types: AverageWeightedQuantileLoss
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
    
    See Also:
    
    AutoMLMetricEnum
  - withMetricName
```
public AutoMLJobObjective withMetricName(AutoMLMetricEnum metricName)
```
    The name of the objective metric used to measure the predictive quality of a machine learning system. During training, the model's parameters are updated iteratively to optimize its performance based on the feedback provided by the objective metric when evaluating the model on the validation dataset.
    
    For the list of all available metrics supported by Autopilot, see Autopilot metrics.
    
    If you do not specify a metric explicitly, the default behavior is to automatically use:
    - For tabular problem types:
      
      Regression: MSE.
      
      Binary classification: F1.
      
      Multiclass classification: Accuracy.
    - For image or text classification problem types: Accuracy
    - For time-series forecasting problem types: AverageWeightedQuantileLoss
    Parameters:
    metricName - The name of the objective metric used to measure the predictive quality of a machine learning system. During training, the model's parameters are updated iteratively to optimize its performance based on the feedback provided by the objective metric when evaluating the model on the validation dataset.
    
    For the list of all available metrics supported by Autopilot, see Autopilot metrics.
    
    If you do not specify a metric explicitly, the default behavior is to automatically use:
    
    For tabular problem types:
    
    Regression: MSE.
    
    Binary classification: F1.
    
    Multiclass classification: Accuracy.
    
    For image or text classification problem types: Accuracy
    
    For time-series forecasting problem types: AverageWeightedQuantileLoss
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
    
    See Also:
    
    AutoMLMetricEnum
  - 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 AutoMLJobObjective 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 - 1.12.544

Class AutoMLJobObjective

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Constructor Detail

AutoMLJobObjective

Method Detail

setMetricName

getMetricName

withMetricName

withMetricName

toString

equals

hashCode

clone

marshall