AutoMLJobConfig (AWS SDK for Java

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AWS SDK for Java 1.x API Reference - 1.12.795

We announced the upcoming end-of-support for AWS SDK for Java (v1). We recommend that you migrate to AWS SDK for Java v2. For dates, additional details, and information on how to migrate, please refer to the linked announcement.

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com.amazonaws.services.sagemaker.model

Class AutoMLJobConfig

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

All Implemented Interfaces:: StructuredPojo, Serializable, Cloneable

@Generated(value="com.amazonaws:aws-java-sdk-code-generator")
public class AutoMLJobConfig
extends Object
implements Serializable, Cloneable, StructuredPojo

A collection of settings used for an AutoML job.

See Also:: AWS API Documentation, Serialized Form

Constructor Summary

Constructors
Constructor and Description

AutoMLJobConfig ()

Constructors
Constructor and Description
`AutoMLJobConfig ()`

Method Summary

All Methods
Modifier and Type	Method and Description
`AutoMLJobConfig`	`clone ()`
`boolean`	`equals (Object obj)`
`AutoMLCandidateGenerationConfig`	`getCandidateGenerationConfig ()` The configuration for generating a candidate for an AutoML job (optional).
`AutoMLJobCompletionCriteria`	`getCompletionCriteria ()` How long an AutoML job is allowed to run, or how many candidates a job is allowed to generate.
`AutoMLDataSplitConfig`	`getDataSplitConfig ()` The configuration for splitting the input training dataset.
`String`	`getMode ()` The method that Autopilot uses to train the data.
`AutoMLSecurityConfig`	`getSecurityConfig ()` The security configuration for traffic encryption or Amazon VPC settings.
`int`	`hashCode ()`
`void`	`marshall (ProtocolMarshaller protocolMarshaller)` Marshalls this structured data using the given `ProtocolMarshaller`.
`void`	`setCandidateGenerationConfig (AutoMLCandidateGenerationConfig candidateGenerationConfig)` The configuration for generating a candidate for an AutoML job (optional).
`void`	`setCompletionCriteria (AutoMLJobCompletionCriteria completionCriteria)` How long an AutoML job is allowed to run, or how many candidates a job is allowed to generate.
`void`	`setDataSplitConfig (AutoMLDataSplitConfig dataSplitConfig)` The configuration for splitting the input training dataset.
`void`	`setMode (String mode)` The method that Autopilot uses to train the data.
`void`	`setSecurityConfig (AutoMLSecurityConfig securityConfig)` The security configuration for traffic encryption or Amazon VPC settings.
`String`	`toString ()` Returns a string representation of this object.
`AutoMLJobConfig`	`withCandidateGenerationConfig (AutoMLCandidateGenerationConfig candidateGenerationConfig)` The configuration for generating a candidate for an AutoML job (optional).
`AutoMLJobConfig`	`withCompletionCriteria (AutoMLJobCompletionCriteria completionCriteria)` How long an AutoML job is allowed to run, or how many candidates a job is allowed to generate.
`AutoMLJobConfig`	`withDataSplitConfig (AutoMLDataSplitConfig dataSplitConfig)` The configuration for splitting the input training dataset.
`AutoMLJobConfig`	`withMode (AutoMLMode mode)` The method that Autopilot uses to train the data.
`AutoMLJobConfig`	`withMode (String mode)` The method that Autopilot uses to train the data.
`AutoMLJobConfig`	`withSecurityConfig (AutoMLSecurityConfig securityConfig)` The security configuration for traffic encryption or Amazon VPC settings.

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

- Constructor Detail
  - AutoMLJobConfig
```
public AutoMLJobConfig()
```
- Method Detail
  - setCompletionCriteria
```
public void setCompletionCriteria(AutoMLJobCompletionCriteria completionCriteria)
```
    How long an AutoML job is allowed to run, or how many candidates a job is allowed to generate.
    
    Parameters:
    
    completionCriteria - How long an AutoML job is allowed to run, or how many candidates a job is allowed to generate.
  - getCompletionCriteria
```
public AutoMLJobCompletionCriteria getCompletionCriteria()
```
    How long an AutoML job is allowed to run, or how many candidates a job is allowed to generate.
    
    Returns:
    
    How long an AutoML job is allowed to run, or how many candidates a job is allowed to generate.
  - withCompletionCriteria
```
public AutoMLJobConfig withCompletionCriteria(AutoMLJobCompletionCriteria completionCriteria)
```
    How long an AutoML job is allowed to run, or how many candidates a job is allowed to generate.
    
    Parameters:
    
    completionCriteria - How long an AutoML job is allowed to run, or how many candidates a job is allowed to generate.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setSecurityConfig
```
public void setSecurityConfig(AutoMLSecurityConfig securityConfig)
```
    The security configuration for traffic encryption or Amazon VPC settings.
    
    Parameters:
    
    securityConfig - The security configuration for traffic encryption or Amazon VPC settings.
  - getSecurityConfig
```
public AutoMLSecurityConfig getSecurityConfig()
```
    The security configuration for traffic encryption or Amazon VPC settings.
    
    Returns:
    
    The security configuration for traffic encryption or Amazon VPC settings.
  - withSecurityConfig
```
public AutoMLJobConfig withSecurityConfig(AutoMLSecurityConfig securityConfig)
```
    The security configuration for traffic encryption or Amazon VPC settings.
    
    Parameters:
    
    securityConfig - The security configuration for traffic encryption or Amazon VPC settings.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setCandidateGenerationConfig
```
public void setCandidateGenerationConfig(AutoMLCandidateGenerationConfig candidateGenerationConfig)
```
    The configuration for generating a candidate for an AutoML job (optional).
    
    Parameters:
    
    candidateGenerationConfig - The configuration for generating a candidate for an AutoML job (optional).
  - getCandidateGenerationConfig
```
public AutoMLCandidateGenerationConfig getCandidateGenerationConfig()
```
    The configuration for generating a candidate for an AutoML job (optional).
    
    Returns:
    
    The configuration for generating a candidate for an AutoML job (optional).
  - withCandidateGenerationConfig
```
public AutoMLJobConfig withCandidateGenerationConfig(AutoMLCandidateGenerationConfig candidateGenerationConfig)
```
    The configuration for generating a candidate for an AutoML job (optional).
    
    Parameters:
    
    candidateGenerationConfig - The configuration for generating a candidate for an AutoML job (optional).
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setDataSplitConfig
```
public void setDataSplitConfig(AutoMLDataSplitConfig dataSplitConfig)
```
    The configuration for splitting the input training dataset.
    
    Type: AutoMLDataSplitConfig
    
    Parameters:
    
    dataSplitConfig - The configuration for splitting the input training dataset.
    
    Type: AutoMLDataSplitConfig
  - getDataSplitConfig
```
public AutoMLDataSplitConfig getDataSplitConfig()
```
    The configuration for splitting the input training dataset.
    
    Type: AutoMLDataSplitConfig
    
    Returns:
    
    The configuration for splitting the input training dataset.
    
    Type: AutoMLDataSplitConfig
  - withDataSplitConfig
```
public AutoMLJobConfig withDataSplitConfig(AutoMLDataSplitConfig dataSplitConfig)
```
    The configuration for splitting the input training dataset.
    
    Type: AutoMLDataSplitConfig
    
    Parameters:
    
    dataSplitConfig - The configuration for splitting the input training dataset.
    
    Type: AutoMLDataSplitConfig
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setMode
```
public void setMode(String mode)
```
    The method that Autopilot uses to train the data. You can either specify the mode manually or let Autopilot choose for you based on the dataset size by selecting AUTO. In AUTO mode, Autopilot chooses ENSEMBLING for datasets smaller than 100 MB, and HYPERPARAMETER_TUNING for larger ones.
    
    The ENSEMBLING mode uses a multi-stack ensemble model to predict classification and regression tasks directly from your dataset. This machine learning mode combines several base models to produce an optimal predictive model. It then uses a stacking ensemble method to combine predictions from contributing members. A multi-stack ensemble model can provide better performance over a single model by combining the predictive capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported by ENSEMBLING mode.
    
    The HYPERPARAMETER_TUNING (HPO) mode uses the best hyperparameters to train the best version of a model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO finds the best hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported by HYPERPARAMETER_TUNING mode.
    
    Parameters:
    
    mode - The method that Autopilot uses to train the data. You can either specify the mode manually or let Autopilot choose for you based on the dataset size by selecting AUTO. In AUTO mode, Autopilot chooses ENSEMBLING for datasets smaller than 100 MB, and HYPERPARAMETER_TUNING for larger ones.
    
    The ENSEMBLING mode uses a multi-stack ensemble model to predict classification and regression tasks directly from your dataset. This machine learning mode combines several base models to produce an optimal predictive model. It then uses a stacking ensemble method to combine predictions from contributing members. A multi-stack ensemble model can provide better performance over a single model by combining the predictive capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported by ENSEMBLING mode.
    
    The HYPERPARAMETER_TUNING (HPO) mode uses the best hyperparameters to train the best version of a model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO finds the best hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported by HYPERPARAMETER_TUNING mode.
    
    See Also:
    
    AutoMLMode
  - getMode
```
public String getMode()
```
    The method that Autopilot uses to train the data. You can either specify the mode manually or let Autopilot choose for you based on the dataset size by selecting AUTO. In AUTO mode, Autopilot chooses ENSEMBLING for datasets smaller than 100 MB, and HYPERPARAMETER_TUNING for larger ones.
    
    The ENSEMBLING mode uses a multi-stack ensemble model to predict classification and regression tasks directly from your dataset. This machine learning mode combines several base models to produce an optimal predictive model. It then uses a stacking ensemble method to combine predictions from contributing members. A multi-stack ensemble model can provide better performance over a single model by combining the predictive capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported by ENSEMBLING mode.
    
    The HYPERPARAMETER_TUNING (HPO) mode uses the best hyperparameters to train the best version of a model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO finds the best hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported by HYPERPARAMETER_TUNING mode.
    
    Returns:
    
    The method that Autopilot uses to train the data. You can either specify the mode manually or let Autopilot choose for you based on the dataset size by selecting AUTO. In AUTO mode, Autopilot chooses ENSEMBLING for datasets smaller than 100 MB, and HYPERPARAMETER_TUNING for larger ones.
    
    The ENSEMBLING mode uses a multi-stack ensemble model to predict classification and regression tasks directly from your dataset. This machine learning mode combines several base models to produce an optimal predictive model. It then uses a stacking ensemble method to combine predictions from contributing members. A multi-stack ensemble model can provide better performance over a single model by combining the predictive capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported by ENSEMBLING mode.
    
    The HYPERPARAMETER_TUNING (HPO) mode uses the best hyperparameters to train the best version of a model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO finds the best hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported by HYPERPARAMETER_TUNING mode.
    
    See Also:
    
    AutoMLMode
  - withMode
```
public AutoMLJobConfig withMode(String mode)
```
    The method that Autopilot uses to train the data. You can either specify the mode manually or let Autopilot choose for you based on the dataset size by selecting AUTO. In AUTO mode, Autopilot chooses ENSEMBLING for datasets smaller than 100 MB, and HYPERPARAMETER_TUNING for larger ones.
    
    The ENSEMBLING mode uses a multi-stack ensemble model to predict classification and regression tasks directly from your dataset. This machine learning mode combines several base models to produce an optimal predictive model. It then uses a stacking ensemble method to combine predictions from contributing members. A multi-stack ensemble model can provide better performance over a single model by combining the predictive capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported by ENSEMBLING mode.
    
    The HYPERPARAMETER_TUNING (HPO) mode uses the best hyperparameters to train the best version of a model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO finds the best hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported by HYPERPARAMETER_TUNING mode.
    
    Parameters:
    
    mode - The method that Autopilot uses to train the data. You can either specify the mode manually or let Autopilot choose for you based on the dataset size by selecting AUTO. In AUTO mode, Autopilot chooses ENSEMBLING for datasets smaller than 100 MB, and HYPERPARAMETER_TUNING for larger ones.
    
    The ENSEMBLING mode uses a multi-stack ensemble model to predict classification and regression tasks directly from your dataset. This machine learning mode combines several base models to produce an optimal predictive model. It then uses a stacking ensemble method to combine predictions from contributing members. A multi-stack ensemble model can provide better performance over a single model by combining the predictive capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported by ENSEMBLING mode.
    
    The HYPERPARAMETER_TUNING (HPO) mode uses the best hyperparameters to train the best version of a model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO finds the best hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported by HYPERPARAMETER_TUNING mode.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
    
    See Also:
    
    AutoMLMode
  - withMode
```
public AutoMLJobConfig withMode(AutoMLMode mode)
```
    The method that Autopilot uses to train the data. You can either specify the mode manually or let Autopilot choose for you based on the dataset size by selecting AUTO. In AUTO mode, Autopilot chooses ENSEMBLING for datasets smaller than 100 MB, and HYPERPARAMETER_TUNING for larger ones.
    
    The ENSEMBLING mode uses a multi-stack ensemble model to predict classification and regression tasks directly from your dataset. This machine learning mode combines several base models to produce an optimal predictive model. It then uses a stacking ensemble method to combine predictions from contributing members. A multi-stack ensemble model can provide better performance over a single model by combining the predictive capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported by ENSEMBLING mode.
    
    The HYPERPARAMETER_TUNING (HPO) mode uses the best hyperparameters to train the best version of a model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO finds the best hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported by HYPERPARAMETER_TUNING mode.
    
    Parameters:
    
    mode - The method that Autopilot uses to train the data. You can either specify the mode manually or let Autopilot choose for you based on the dataset size by selecting AUTO. In AUTO mode, Autopilot chooses ENSEMBLING for datasets smaller than 100 MB, and HYPERPARAMETER_TUNING for larger ones.
    
    The ENSEMBLING mode uses a multi-stack ensemble model to predict classification and regression tasks directly from your dataset. This machine learning mode combines several base models to produce an optimal predictive model. It then uses a stacking ensemble method to combine predictions from contributing members. A multi-stack ensemble model can provide better performance over a single model by combining the predictive capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported by ENSEMBLING mode.
    
    The HYPERPARAMETER_TUNING (HPO) mode uses the best hyperparameters to train the best version of a model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO finds the best hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported by HYPERPARAMETER_TUNING mode.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
    
    See Also:
    
    AutoMLMode
  - 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 AutoMLJobConfig 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.

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Constr |
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AWS SDK for Java 1.x API Reference - 1.12.795

Class AutoMLJobConfig

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Constructor Detail

AutoMLJobConfig

Method Detail

setCompletionCriteria

getCompletionCriteria

withCompletionCriteria

setSecurityConfig

getSecurityConfig

withSecurityConfig

setCandidateGenerationConfig

getCandidateGenerationConfig

withCandidateGenerationConfig

setDataSplitConfig

getDataSplitConfig

withDataSplitConfig

setMode

getMode

withMode

withMode

toString

equals

hashCode

clone

marshall