Class AutoMLJobConfig
- All Implemented Interfaces:
Serializable
,SdkPojo
,ToCopyableBuilder<AutoMLJobConfig.Builder,
AutoMLJobConfig>
A collection of settings used for an AutoML job.
- See Also:
-
Nested Class Summary
-
Method Summary
Modifier and TypeMethodDescriptionstatic AutoMLJobConfig.Builder
builder()
The configuration for generating a candidate for an AutoML job (optional).How long an AutoML job is allowed to run, or how many candidates a job is allowed to generate.final AutoMLDataSplitConfig
The configuration for splitting the input training dataset.final boolean
final boolean
equalsBySdkFields
(Object obj) Indicates whether some other object is "equal to" this one by SDK fields.final <T> Optional
<T> getValueForField
(String fieldName, Class<T> clazz) final int
hashCode()
final AutoMLMode
mode()
The method that Autopilot uses to train the data.final String
The method that Autopilot uses to train the data.final AutoMLSecurityConfig
The security configuration for traffic encryption or Amazon VPC settings.static Class
<? extends AutoMLJobConfig.Builder> Take this object and create a builder that contains all of the current property values of this object.final String
toString()
Returns a string representation of this object.Methods inherited from interface software.amazon.awssdk.utils.builder.ToCopyableBuilder
copy
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Method Details
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completionCriteria
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.
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securityConfig
The security configuration for traffic encryption or Amazon VPC settings.
- Returns:
- The security configuration for traffic encryption or Amazon VPC settings.
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candidateGenerationConfig
The configuration for generating a candidate for an AutoML job (optional).
- Returns:
- The configuration for generating a candidate for an AutoML job (optional).
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dataSplitConfig
The configuration for splitting the input training dataset.
Type: AutoMLDataSplitConfig
- Returns:
- The configuration for splitting the input training dataset.
Type: AutoMLDataSplitConfig
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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
. InAUTO
mode, Autopilot choosesENSEMBLING
for datasets smaller than 100 MB, andHYPERPARAMETER_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 byENSEMBLING
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 byHYPERPARAMETER_TUNING
mode.If the service returns an enum value that is not available in the current SDK version,
mode
will returnAutoMLMode.UNKNOWN_TO_SDK_VERSION
. The raw value returned by the service is available frommodeAsString()
.- 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
. InAUTO
mode, Autopilot choosesENSEMBLING
for datasets smaller than 100 MB, andHYPERPARAMETER_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 byENSEMBLING
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 byHYPERPARAMETER_TUNING
mode. - See Also:
-
modeAsString
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
. InAUTO
mode, Autopilot choosesENSEMBLING
for datasets smaller than 100 MB, andHYPERPARAMETER_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 byENSEMBLING
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 byHYPERPARAMETER_TUNING
mode.If the service returns an enum value that is not available in the current SDK version,
mode
will returnAutoMLMode.UNKNOWN_TO_SDK_VERSION
. The raw value returned by the service is available frommodeAsString()
.- 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
. InAUTO
mode, Autopilot choosesENSEMBLING
for datasets smaller than 100 MB, andHYPERPARAMETER_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 byENSEMBLING
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 byHYPERPARAMETER_TUNING
mode. - See Also:
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toBuilder
Description copied from interface:ToCopyableBuilder
Take this object and create a builder that contains all of the current property values of this object.- Specified by:
toBuilder
in interfaceToCopyableBuilder<AutoMLJobConfig.Builder,
AutoMLJobConfig> - Returns:
- a builder for type T
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builder
-
serializableBuilderClass
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hashCode
public final int hashCode() -
equals
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equalsBySdkFields
Description copied from interface:SdkPojo
Indicates whether some other object is "equal to" this one by SDK fields. An SDK field is a modeled, non-inherited field in anSdkPojo
class, and is generated based on a service model.If an
SdkPojo
class does not have any inherited fields,equalsBySdkFields
andequals
are essentially the same.- Specified by:
equalsBySdkFields
in interfaceSdkPojo
- Parameters:
obj
- the object to be compared with- Returns:
- true if the other object equals to this object by sdk fields, false otherwise.
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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. -
getValueForField
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sdkFields
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