com.amazonaws.services.sagemaker.model

Class TabularJobConfig

java.lang.Object

com.amazonaws.services.sagemaker.model.TabularJobConfig

All Implemented Interfaces:

StructuredPojo, Serializable, Cloneable

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

The collection of settings used by an AutoML job V2 for the tabular problem type.

See Also:

AWS API Documentation, Serialized Form

Constructor Summary

Constructors

Constructor and Description
TabularJobConfig()

Method Summary

Modifier and Type	Method and Description
TabularJobConfig	clone()
boolean	equals(Object obj)
CandidateGenerationConfig	getCandidateGenerationConfig() The configuration information of how model candidates are generated.
AutoMLJobCompletionCriteria	getCompletionCriteria()
String	getFeatureSpecificationS3Uri() A URL to the Amazon S3 data source containing selected features from the input data source to run an Autopilot job V2.
Boolean	getGenerateCandidateDefinitionsOnly() Generates possible candidates without training the models.
String	getMode() The method that Autopilot uses to train the data.
String	getProblemType() The type of supervised learning problem available for the model candidates of the AutoML job V2.
String	getSampleWeightAttributeName() If specified, this column name indicates which column of the dataset should be treated as sample weights for use by the objective metric during the training, evaluation, and the selection of the best model.
String	getTargetAttributeName() The name of the target variable in supervised learning, usually represented by 'y'.
int	hashCode()
Boolean	isGenerateCandidateDefinitionsOnly() Generates possible candidates without training the models.
void	marshall(ProtocolMarshaller protocolMarshaller) Marshalls this structured data using the given ProtocolMarshaller.
void	setCandidateGenerationConfig(CandidateGenerationConfig candidateGenerationConfig) The configuration information of how model candidates are generated.
void	setCompletionCriteria(AutoMLJobCompletionCriteria completionCriteria)
void	setFeatureSpecificationS3Uri(String featureSpecificationS3Uri) A URL to the Amazon S3 data source containing selected features from the input data source to run an Autopilot job V2.
void	setGenerateCandidateDefinitionsOnly(Boolean generateCandidateDefinitionsOnly) Generates possible candidates without training the models.
void	setMode(String mode) The method that Autopilot uses to train the data.
void	setProblemType(String problemType) The type of supervised learning problem available for the model candidates of the AutoML job V2.
void	setSampleWeightAttributeName(String sampleWeightAttributeName) If specified, this column name indicates which column of the dataset should be treated as sample weights for use by the objective metric during the training, evaluation, and the selection of the best model.
void	setTargetAttributeName(String targetAttributeName) The name of the target variable in supervised learning, usually represented by 'y'.
String	toString() Returns a string representation of this object.
TabularJobConfig	withCandidateGenerationConfig(CandidateGenerationConfig candidateGenerationConfig) The configuration information of how model candidates are generated.
TabularJobConfig	withCompletionCriteria(AutoMLJobCompletionCriteria completionCriteria)
TabularJobConfig	withFeatureSpecificationS3Uri(String featureSpecificationS3Uri) A URL to the Amazon S3 data source containing selected features from the input data source to run an Autopilot job V2.
TabularJobConfig	withGenerateCandidateDefinitionsOnly(Boolean generateCandidateDefinitionsOnly) Generates possible candidates without training the models.
TabularJobConfig	withMode(AutoMLMode mode) The method that Autopilot uses to train the data.
TabularJobConfig	withMode(String mode) The method that Autopilot uses to train the data.
TabularJobConfig	withProblemType(ProblemType problemType) The type of supervised learning problem available for the model candidates of the AutoML job V2.
TabularJobConfig	withProblemType(String problemType) The type of supervised learning problem available for the model candidates of the AutoML job V2.
TabularJobConfig	withSampleWeightAttributeName(String sampleWeightAttributeName) If specified, this column name indicates which column of the dataset should be treated as sample weights for use by the objective metric during the training, evaluation, and the selection of the best model.
TabularJobConfig	withTargetAttributeName(String targetAttributeName) The name of the target variable in supervised learning, usually represented by 'y'.

Methods inherited from class java.lang.Object

getClass, notify, notifyAll, wait, wait, wait

Constructor Detail

TabularJobConfig

public TabularJobConfig()

Method Detail

setCandidateGenerationConfig

public void setCandidateGenerationConfig(CandidateGenerationConfig candidateGenerationConfig)

The configuration information of how model candidates are generated.

Parameters:

candidateGenerationConfig - The configuration information of how model candidates are generated.

getCandidateGenerationConfig

public CandidateGenerationConfig getCandidateGenerationConfig()

The configuration information of how model candidates are generated.

Returns:

The configuration information of how model candidates are generated.

withCandidateGenerationConfig

public TabularJobConfig withCandidateGenerationConfig(CandidateGenerationConfig candidateGenerationConfig)

The configuration information of how model candidates are generated.

Parameters:

candidateGenerationConfig - The configuration information of how model candidates are generated.

Returns:

Returns a reference to this object so that method calls can be chained together.

setCompletionCriteria

public void setCompletionCriteria(AutoMLJobCompletionCriteria completionCriteria)

Parameters:

completionCriteria -

getCompletionCriteria

public AutoMLJobCompletionCriteria getCompletionCriteria()

Returns:

withCompletionCriteria

public TabularJobConfig withCompletionCriteria(AutoMLJobCompletionCriteria completionCriteria)

Parameters:

completionCriteria -

Returns:

Returns a reference to this object so that method calls can be chained together.

setFeatureSpecificationS3Uri

public void setFeatureSpecificationS3Uri(String featureSpecificationS3Uri)

A URL to the Amazon S3 data source containing selected features from the input data source to run an Autopilot job V2. You can input FeatureAttributeNames (optional) in JSON format as shown below:

{ "FeatureAttributeNames":["col1", "col2", ...] }.

You can also specify the data type of the feature (optional) in the format shown below:

{ "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } }

These column keys may not include the target column.

In ensembling mode, Autopilot only supports the following data types: numeric, categorical, text, and datetime. In HPO mode, Autopilot can support numeric, categorical, text, datetime, and sequence.

If only FeatureDataTypes is provided, the column keys (col1, col2,..) should be a subset of the column names in the input data.

If both FeatureDataTypes and FeatureAttributeNames are provided, then the column keys should be a subset of the column names provided in FeatureAttributeNames.

The key name FeatureAttributeNames is fixed. The values listed in ["col1", "col2", ...] are case sensitive and should be a list of strings containing unique values that are a subset of the column names in the input data. The list of columns provided must not include the target column.

Parameters:

featureSpecificationS3Uri - A URL to the Amazon S3 data source containing selected features from the input data source to run an Autopilot job V2. You can input FeatureAttributeNames (optional) in JSON format as shown below:

{ "FeatureAttributeNames":["col1", "col2", ...] }.

You can also specify the data type of the feature (optional) in the format shown below:

{ "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } }

These column keys may not include the target column.

In ensembling mode, Autopilot only supports the following data types: numeric, categorical, text, and datetime. In HPO mode, Autopilot can support numeric, categorical, text, datetime, and sequence.

If only FeatureDataTypes is provided, the column keys (col1, col2 ,..) should be a subset of the column names in the input data.

If both FeatureDataTypes and FeatureAttributeNames are provided, then the column keys should be a subset of the column names provided in FeatureAttributeNames.

The key name FeatureAttributeNames is fixed. The values listed in ["col1", "col2", ...] are case sensitive and should be a list of strings containing unique values that are a subset of the column names in the input data. The list of columns provided must not include the target column.

getFeatureSpecificationS3Uri

public String getFeatureSpecificationS3Uri()

A URL to the Amazon S3 data source containing selected features from the input data source to run an Autopilot job V2. You can input FeatureAttributeNames (optional) in JSON format as shown below:

{ "FeatureAttributeNames":["col1", "col2", ...] }.

You can also specify the data type of the feature (optional) in the format shown below:

{ "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } }

These column keys may not include the target column.

In ensembling mode, Autopilot only supports the following data types: numeric, categorical, text, and datetime. In HPO mode, Autopilot can support numeric, categorical, text, datetime, and sequence.

If only FeatureDataTypes is provided, the column keys (col1, col2,..) should be a subset of the column names in the input data.

If both FeatureDataTypes and FeatureAttributeNames are provided, then the column keys should be a subset of the column names provided in FeatureAttributeNames.

The key name FeatureAttributeNames is fixed. The values listed in ["col1", "col2", ...] are case sensitive and should be a list of strings containing unique values that are a subset of the column names in the input data. The list of columns provided must not include the target column.

Returns:

A URL to the Amazon S3 data source containing selected features from the input data source to run an Autopilot job V2. You can input FeatureAttributeNames (optional) in JSON format as shown below:

{ "FeatureAttributeNames":["col1", "col2", ...] }.

You can also specify the data type of the feature (optional) in the format shown below:

{ "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } }

These column keys may not include the target column.

In ensembling mode, Autopilot only supports the following data types: numeric, categorical, text, and datetime. In HPO mode, Autopilot can support numeric, categorical, text, datetime, and sequence.

If only FeatureDataTypes is provided, the column keys (col1, col2 ,..) should be a subset of the column names in the input data.

If both FeatureDataTypes and FeatureAttributeNames are provided, then the column keys should be a subset of the column names provided in FeatureAttributeNames.

The key name FeatureAttributeNames is fixed. The values listed in ["col1", "col2", ...] are case sensitive and should be a list of strings containing unique values that are a subset of the column names in the input data. The list of columns provided must not include the target column.

withFeatureSpecificationS3Uri

public TabularJobConfig withFeatureSpecificationS3Uri(String featureSpecificationS3Uri)

A URL to the Amazon S3 data source containing selected features from the input data source to run an Autopilot job V2. You can input FeatureAttributeNames (optional) in JSON format as shown below:

{ "FeatureAttributeNames":["col1", "col2", ...] }.

You can also specify the data type of the feature (optional) in the format shown below:

{ "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } }

These column keys may not include the target column.

In ensembling mode, Autopilot only supports the following data types: numeric, categorical, text, and datetime. In HPO mode, Autopilot can support numeric, categorical, text, datetime, and sequence.

If only FeatureDataTypes is provided, the column keys (col1, col2,..) should be a subset of the column names in the input data.

If both FeatureDataTypes and FeatureAttributeNames are provided, then the column keys should be a subset of the column names provided in FeatureAttributeNames.

The key name FeatureAttributeNames is fixed. The values listed in ["col1", "col2", ...] are case sensitive and should be a list of strings containing unique values that are a subset of the column names in the input data. The list of columns provided must not include the target column.

Parameters:

featureSpecificationS3Uri - A URL to the Amazon S3 data source containing selected features from the input data source to run an Autopilot job V2. You can input FeatureAttributeNames (optional) in JSON format as shown below:

{ "FeatureAttributeNames":["col1", "col2", ...] }.

You can also specify the data type of the feature (optional) in the format shown below:

{ "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } }

These column keys may not include the target column.

In ensembling mode, Autopilot only supports the following data types: numeric, categorical, text, and datetime. In HPO mode, Autopilot can support numeric, categorical, text, datetime, and sequence.

If only FeatureDataTypes is provided, the column keys (col1, col2 ,..) should be a subset of the column names in the input data.

If both FeatureDataTypes and FeatureAttributeNames are provided, then the column keys should be a subset of the column names provided in FeatureAttributeNames.

The key name FeatureAttributeNames is fixed. The values listed in ["col1", "col2", ...] are case sensitive and should be a list of strings containing unique values that are a subset of the column names in the input data. The list of columns provided must not include the target column.

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 TabularJobConfig 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 TabularJobConfig 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

setGenerateCandidateDefinitionsOnly

public void setGenerateCandidateDefinitionsOnly(Boolean generateCandidateDefinitionsOnly)

Generates possible candidates without training the models. A model candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.

Parameters:

generateCandidateDefinitionsOnly - Generates possible candidates without training the models. A model candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.

getGenerateCandidateDefinitionsOnly

public Boolean getGenerateCandidateDefinitionsOnly()

Generates possible candidates without training the models. A model candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.

Returns:

Generates possible candidates without training the models. A model candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.

withGenerateCandidateDefinitionsOnly

public TabularJobConfig withGenerateCandidateDefinitionsOnly(Boolean generateCandidateDefinitionsOnly)

Generates possible candidates without training the models. A model candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.

Parameters:

generateCandidateDefinitionsOnly - Generates possible candidates without training the models. A model candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.

Returns:

Returns a reference to this object so that method calls can be chained together.

isGenerateCandidateDefinitionsOnly

public Boolean isGenerateCandidateDefinitionsOnly()

Generates possible candidates without training the models. A model candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.

Returns:

Generates possible candidates without training the models. A model candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.

setProblemType

public void setProblemType(String problemType)

The type of supervised learning problem available for the model candidates of the AutoML job V2. For more information, see SageMaker Autopilot problem types.

You must either specify the type of supervised learning problem in ProblemType and provide the AutoMLJobObjective metric, or none at all.

Parameters:

problemType - The type of supervised learning problem available for the model candidates of the AutoML job V2. For more information, see SageMaker Autopilot problem types.

You must either specify the type of supervised learning problem in ProblemType and provide the AutoMLJobObjective metric, or none at all.

See Also:

ProblemType

getProblemType

public String getProblemType()

The type of supervised learning problem available for the model candidates of the AutoML job V2. For more information, see SageMaker Autopilot problem types.

You must either specify the type of supervised learning problem in ProblemType and provide the AutoMLJobObjective metric, or none at all.

Returns:

The type of supervised learning problem available for the model candidates of the AutoML job V2. For more information, see SageMaker Autopilot problem types.

You must either specify the type of supervised learning problem in ProblemType and provide the AutoMLJobObjective metric, or none at all.

See Also:

ProblemType

withProblemType

public TabularJobConfig withProblemType(String problemType)

The type of supervised learning problem available for the model candidates of the AutoML job V2. For more information, see SageMaker Autopilot problem types.

You must either specify the type of supervised learning problem in ProblemType and provide the AutoMLJobObjective metric, or none at all.

Parameters:

problemType - The type of supervised learning problem available for the model candidates of the AutoML job V2. For more information, see SageMaker Autopilot problem types.

You must either specify the type of supervised learning problem in ProblemType and provide the AutoMLJobObjective metric, or none at all.

Returns:

Returns a reference to this object so that method calls can be chained together.

See Also:

ProblemType

withProblemType

public TabularJobConfig withProblemType(ProblemType problemType)

The type of supervised learning problem available for the model candidates of the AutoML job V2. For more information, see SageMaker Autopilot problem types.

You must either specify the type of supervised learning problem in ProblemType and provide the AutoMLJobObjective metric, or none at all.

Parameters:

problemType - The type of supervised learning problem available for the model candidates of the AutoML job V2. For more information, see SageMaker Autopilot problem types.

You must either specify the type of supervised learning problem in ProblemType and provide the AutoMLJobObjective metric, or none at all.

Returns:

Returns a reference to this object so that method calls can be chained together.

See Also:

ProblemType

setTargetAttributeName

public void setTargetAttributeName(String targetAttributeName)

The name of the target variable in supervised learning, usually represented by 'y'.

Parameters:

targetAttributeName - The name of the target variable in supervised learning, usually represented by 'y'.

getTargetAttributeName

public String getTargetAttributeName()

The name of the target variable in supervised learning, usually represented by 'y'.

Returns:

The name of the target variable in supervised learning, usually represented by 'y'.

withTargetAttributeName

public TabularJobConfig withTargetAttributeName(String targetAttributeName)

The name of the target variable in supervised learning, usually represented by 'y'.

Parameters:

targetAttributeName - The name of the target variable in supervised learning, usually represented by 'y'.

Returns:

Returns a reference to this object so that method calls can be chained together.

setSampleWeightAttributeName

public void setSampleWeightAttributeName(String sampleWeightAttributeName)

If specified, this column name indicates which column of the dataset should be treated as sample weights for use by the objective metric during the training, evaluation, and the selection of the best model. This column is not considered as a predictive feature. For more information on Autopilot metrics, see Metrics and validation.

Sample weights should be numeric, non-negative, with larger values indicating which rows are more important than others. Data points that have invalid or no weight value are excluded.

Support for sample weights is available in Ensembling mode only.

Parameters:

sampleWeightAttributeName - If specified, this column name indicates which column of the dataset should be treated as sample weights for use by the objective metric during the training, evaluation, and the selection of the best model. This column is not considered as a predictive feature. For more information on Autopilot metrics, see Metrics and validation.

Sample weights should be numeric, non-negative, with larger values indicating which rows are more important than others. Data points that have invalid or no weight value are excluded.

Support for sample weights is available in Ensembling mode only.

getSampleWeightAttributeName

public String getSampleWeightAttributeName()

If specified, this column name indicates which column of the dataset should be treated as sample weights for use by the objective metric during the training, evaluation, and the selection of the best model. This column is not considered as a predictive feature. For more information on Autopilot metrics, see Metrics and validation.

Sample weights should be numeric, non-negative, with larger values indicating which rows are more important than others. Data points that have invalid or no weight value are excluded.

Support for sample weights is available in Ensembling mode only.

Returns:

If specified, this column name indicates which column of the dataset should be treated as sample weights for use by the objective metric during the training, evaluation, and the selection of the best model. This column is not considered as a predictive feature. For more information on Autopilot metrics, see Metrics and validation.

Sample weights should be numeric, non-negative, with larger values indicating which rows are more important than others. Data points that have invalid or no weight value are excluded.

Support for sample weights is available in Ensembling mode only.

withSampleWeightAttributeName

public TabularJobConfig withSampleWeightAttributeName(String sampleWeightAttributeName)

If specified, this column name indicates which column of the dataset should be treated as sample weights for use by the objective metric during the training, evaluation, and the selection of the best model. This column is not considered as a predictive feature. For more information on Autopilot metrics, see Metrics and validation.

Sample weights should be numeric, non-negative, with larger values indicating which rows are more important than others. Data points that have invalid or no weight value are excluded.

Support for sample weights is available in Ensembling mode only.

Parameters:

sampleWeightAttributeName - If specified, this column name indicates which column of the dataset should be treated as sample weights for use by the objective metric during the training, evaluation, and the selection of the best model. This column is not considered as a predictive feature. For more information on Autopilot metrics, see Metrics and validation.

Sample weights should be numeric, non-negative, with larger values indicating which rows are more important than others. Data points that have invalid or no weight value are excluded.

Support for sample weights is available in Ensembling mode only.

Returns:

Returns a reference to this object so that method calls can be chained together.

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 TabularJobConfig 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.