Class GetMLModelResult

java.lang.Object
com.amazonaws.services.machinelearning.model.GetMLModelResult
All Implemented Interfaces:
Serializable, Cloneable

public class GetMLModelResult extends Object implements Serializable, Cloneable

Represents the output of a GetMLModel operation, and provides detailed information about a MLModel.

See Also:
  • Constructor Details

    • GetMLModelResult

      public GetMLModelResult()
  • Method Details

    • setMLModelId

      public void setMLModelId(String mLModelId)

      The MLModel ID which is same as the MLModelId in the request.

      Parameters:
      mLModelId - The MLModel ID which is same as the MLModelId in the request.
    • getMLModelId

      public String getMLModelId()

      The MLModel ID which is same as the MLModelId in the request.

      Returns:
      The MLModel ID which is same as the MLModelId in the request.
    • withMLModelId

      public GetMLModelResult withMLModelId(String mLModelId)

      The MLModel ID which is same as the MLModelId in the request.

      Parameters:
      mLModelId - The MLModel ID which is same as the MLModelId in the request.
      Returns:
      Returns a reference to this object so that method calls can be chained together.
    • setTrainingDataSourceId

      public void setTrainingDataSourceId(String trainingDataSourceId)

      The ID of the training DataSource.

      Parameters:
      trainingDataSourceId - The ID of the training DataSource.
    • getTrainingDataSourceId

      public String getTrainingDataSourceId()

      The ID of the training DataSource.

      Returns:
      The ID of the training DataSource.
    • withTrainingDataSourceId

      public GetMLModelResult withTrainingDataSourceId(String trainingDataSourceId)

      The ID of the training DataSource.

      Parameters:
      trainingDataSourceId - The ID of the training DataSource.
      Returns:
      Returns a reference to this object so that method calls can be chained together.
    • setCreatedByIamUser

      public void setCreatedByIamUser(String createdByIamUser)

      The AWS user account from which the MLModel was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.

      Parameters:
      createdByIamUser - The AWS user account from which the MLModel was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.
    • getCreatedByIamUser

      public String getCreatedByIamUser()

      The AWS user account from which the MLModel was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.

      Returns:
      The AWS user account from which the MLModel was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.
    • withCreatedByIamUser

      public GetMLModelResult withCreatedByIamUser(String createdByIamUser)

      The AWS user account from which the MLModel was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.

      Parameters:
      createdByIamUser - The AWS user account from which the MLModel was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.
      Returns:
      Returns a reference to this object so that method calls can be chained together.
    • setCreatedAt

      public void setCreatedAt(Date createdAt)

      The time that the MLModel was created. The time is expressed in epoch time.

      Parameters:
      createdAt - The time that the MLModel was created. The time is expressed in epoch time.
    • getCreatedAt

      public Date getCreatedAt()

      The time that the MLModel was created. The time is expressed in epoch time.

      Returns:
      The time that the MLModel was created. The time is expressed in epoch time.
    • withCreatedAt

      public GetMLModelResult withCreatedAt(Date createdAt)

      The time that the MLModel was created. The time is expressed in epoch time.

      Parameters:
      createdAt - The time that the MLModel was created. The time is expressed in epoch time.
      Returns:
      Returns a reference to this object so that method calls can be chained together.
    • setLastUpdatedAt

      public void setLastUpdatedAt(Date lastUpdatedAt)

      The time of the most recent edit to the MLModel. The time is expressed in epoch time.

      Parameters:
      lastUpdatedAt - The time of the most recent edit to the MLModel. The time is expressed in epoch time.
    • getLastUpdatedAt

      public Date getLastUpdatedAt()

      The time of the most recent edit to the MLModel. The time is expressed in epoch time.

      Returns:
      The time of the most recent edit to the MLModel. The time is expressed in epoch time.
    • withLastUpdatedAt

      public GetMLModelResult withLastUpdatedAt(Date lastUpdatedAt)

      The time of the most recent edit to the MLModel. The time is expressed in epoch time.

      Parameters:
      lastUpdatedAt - The time of the most recent edit to the MLModel. The time is expressed in epoch time.
      Returns:
      Returns a reference to this object so that method calls can be chained together.
    • setName

      public void setName(String name)

      A user-supplied name or description of the MLModel.

      Parameters:
      name - A user-supplied name or description of the MLModel.
    • getName

      public String getName()

      A user-supplied name or description of the MLModel.

      Returns:
      A user-supplied name or description of the MLModel.
    • withName

      public GetMLModelResult withName(String name)

      A user-supplied name or description of the MLModel.

      Parameters:
      name - A user-supplied name or description of the MLModel.
      Returns:
      Returns a reference to this object so that method calls can be chained together.
    • setStatus

      public void setStatus(String status)

      The current status of the MLModel. This element can have one of the following values:

      • PENDING - Amazon Machine Learning (Amazon ML) submitted a request to describe a MLModel.
      • INPROGRESS - The request is processing.
      • FAILED - The request did not run to completion. It is not usable.
      • COMPLETED - The request completed successfully.
      • DELETED - The MLModel is marked as deleted. It is not usable.
      Parameters:
      status - The current status of the MLModel. This element can have one of the following values:

      • PENDING - Amazon Machine Learning (Amazon ML) submitted a request to describe a MLModel.
      • INPROGRESS - The request is processing.
      • FAILED - The request did not run to completion. It is not usable.
      • COMPLETED - The request completed successfully.
      • DELETED - The MLModel is marked as deleted. It is not usable.
      See Also:
    • getStatus

      public String getStatus()

      The current status of the MLModel. This element can have one of the following values:

      • PENDING - Amazon Machine Learning (Amazon ML) submitted a request to describe a MLModel.
      • INPROGRESS - The request is processing.
      • FAILED - The request did not run to completion. It is not usable.
      • COMPLETED - The request completed successfully.
      • DELETED - The MLModel is marked as deleted. It is not usable.
      Returns:
      The current status of the MLModel. This element can have one of the following values:

      • PENDING - Amazon Machine Learning (Amazon ML) submitted a request to describe a MLModel.
      • INPROGRESS - The request is processing.
      • FAILED - The request did not run to completion. It is not usable.
      • COMPLETED - The request completed successfully.
      • DELETED - The MLModel is marked as deleted. It is not usable.
      See Also:
    • withStatus

      public GetMLModelResult withStatus(String status)

      The current status of the MLModel. This element can have one of the following values:

      • PENDING - Amazon Machine Learning (Amazon ML) submitted a request to describe a MLModel.
      • INPROGRESS - The request is processing.
      • FAILED - The request did not run to completion. It is not usable.
      • COMPLETED - The request completed successfully.
      • DELETED - The MLModel is marked as deleted. It is not usable.
      Parameters:
      status - The current status of the MLModel. This element can have one of the following values:

      • PENDING - Amazon Machine Learning (Amazon ML) submitted a request to describe a MLModel.
      • INPROGRESS - The request is processing.
      • FAILED - The request did not run to completion. It is not usable.
      • COMPLETED - The request completed successfully.
      • DELETED - The MLModel is marked as deleted. It is not usable.
      Returns:
      Returns a reference to this object so that method calls can be chained together.
      See Also:
    • setStatus

      public void setStatus(EntityStatus status)

      The current status of the MLModel. This element can have one of the following values:

      • PENDING - Amazon Machine Learning (Amazon ML) submitted a request to describe a MLModel.
      • INPROGRESS - The request is processing.
      • FAILED - The request did not run to completion. It is not usable.
      • COMPLETED - The request completed successfully.
      • DELETED - The MLModel is marked as deleted. It is not usable.
      Parameters:
      status - The current status of the MLModel. This element can have one of the following values:

      • PENDING - Amazon Machine Learning (Amazon ML) submitted a request to describe a MLModel.
      • INPROGRESS - The request is processing.
      • FAILED - The request did not run to completion. It is not usable.
      • COMPLETED - The request completed successfully.
      • DELETED - The MLModel is marked as deleted. It is not usable.
      See Also:
    • withStatus

      public GetMLModelResult withStatus(EntityStatus status)

      The current status of the MLModel. This element can have one of the following values:

      • PENDING - Amazon Machine Learning (Amazon ML) submitted a request to describe a MLModel.
      • INPROGRESS - The request is processing.
      • FAILED - The request did not run to completion. It is not usable.
      • COMPLETED - The request completed successfully.
      • DELETED - The MLModel is marked as deleted. It is not usable.
      Parameters:
      status - The current status of the MLModel. This element can have one of the following values:

      • PENDING - Amazon Machine Learning (Amazon ML) submitted a request to describe a MLModel.
      • INPROGRESS - The request is processing.
      • FAILED - The request did not run to completion. It is not usable.
      • COMPLETED - The request completed successfully.
      • DELETED - The MLModel is marked as deleted. It is not usable.
      Returns:
      Returns a reference to this object so that method calls can be chained together.
      See Also:
    • setSizeInBytes

      public void setSizeInBytes(Long sizeInBytes)
      Parameters:
      sizeInBytes -
    • getSizeInBytes

      public Long getSizeInBytes()
      Returns:
    • withSizeInBytes

      public GetMLModelResult withSizeInBytes(Long sizeInBytes)
      Parameters:
      sizeInBytes -
      Returns:
      Returns a reference to this object so that method calls can be chained together.
    • setEndpointInfo

      public void setEndpointInfo(RealtimeEndpointInfo endpointInfo)

      The current endpoint of the MLModel

      Parameters:
      endpointInfo - The current endpoint of the MLModel
    • getEndpointInfo

      public RealtimeEndpointInfo getEndpointInfo()

      The current endpoint of the MLModel

      Returns:
      The current endpoint of the MLModel
    • withEndpointInfo

      public GetMLModelResult withEndpointInfo(RealtimeEndpointInfo endpointInfo)

      The current endpoint of the MLModel

      Parameters:
      endpointInfo - The current endpoint of the MLModel
      Returns:
      Returns a reference to this object so that method calls can be chained together.
    • getTrainingParameters

      public Map<String,String> getTrainingParameters()

      A list of the training parameters in the MLModel. The list is implemented as a map of key/value pairs.

      The following is the current set of training parameters:

      • sgd.l1RegularizationAmount - Coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse feature set. If you use this parameter, specify a small value, such as 1.0E-04 or 1.0E-08.

        The value is a double that ranges from 0 to MAX_DOUBLE. The default is not to use L1 normalization. The parameter cannot be used when L2 is specified. Use this parameter sparingly.

      • sgd.l2RegularizationAmount - Coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, specify a small value, such as 1.0E-04 or 1.0E-08.

        The value is a double that ranges from 0 to MAX_DOUBLE. The default is not to use L2 normalization. This parameter cannot be used when L1 is specified. Use this parameter sparingly.

      • sgd.maxPasses - The number of times that the training process traverses the observations to build the MLModel. The value is an integer that ranges from 1 to 10000. The default value is 10.

      • sgd.maxMLModelSizeInBytes - The maximum allowed size of the model. Depending on the input data, the model size might affect performance.

        The value is an integer that ranges from 100000 to 2147483648. The default value is 33554432.

      Returns:
      A list of the training parameters in the MLModel. The list is implemented as a map of key/value pairs.

      The following is the current set of training parameters:

      • sgd.l1RegularizationAmount - Coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse feature set. If you use this parameter, specify a small value, such as 1.0E-04 or 1.0E-08.

        The value is a double that ranges from 0 to MAX_DOUBLE. The default is not to use L1 normalization. The parameter cannot be used when L2 is specified. Use this parameter sparingly.

      • sgd.l2RegularizationAmount - Coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, specify a small value, such as 1.0E-04 or 1.0E-08.

        The value is a double that ranges from 0 to MAX_DOUBLE. The default is not to use L2 normalization. This parameter cannot be used when L1 is specified. Use this parameter sparingly.

      • sgd.maxPasses - The number of times that the training process traverses the observations to build the MLModel. The value is an integer that ranges from 1 to 10000. The default value is 10.

      • sgd.maxMLModelSizeInBytes - The maximum allowed size of the model. Depending on the input data, the model size might affect performance.

        The value is an integer that ranges from 100000 to 2147483648. The default value is 33554432.

    • setTrainingParameters

      public void setTrainingParameters(Map<String,String> trainingParameters)

      A list of the training parameters in the MLModel. The list is implemented as a map of key/value pairs.

      The following is the current set of training parameters:

      • sgd.l1RegularizationAmount - Coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse feature set. If you use this parameter, specify a small value, such as 1.0E-04 or 1.0E-08.

        The value is a double that ranges from 0 to MAX_DOUBLE. The default is not to use L1 normalization. The parameter cannot be used when L2 is specified. Use this parameter sparingly.

      • sgd.l2RegularizationAmount - Coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, specify a small value, such as 1.0E-04 or 1.0E-08.

        The value is a double that ranges from 0 to MAX_DOUBLE. The default is not to use L2 normalization. This parameter cannot be used when L1 is specified. Use this parameter sparingly.

      • sgd.maxPasses - The number of times that the training process traverses the observations to build the MLModel. The value is an integer that ranges from 1 to 10000. The default value is 10.

      • sgd.maxMLModelSizeInBytes - The maximum allowed size of the model. Depending on the input data, the model size might affect performance.

        The value is an integer that ranges from 100000 to 2147483648. The default value is 33554432.

      Parameters:
      trainingParameters - A list of the training parameters in the MLModel. The list is implemented as a map of key/value pairs.

      The following is the current set of training parameters:

      • sgd.l1RegularizationAmount - Coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse feature set. If you use this parameter, specify a small value, such as 1.0E-04 or 1.0E-08.

        The value is a double that ranges from 0 to MAX_DOUBLE. The default is not to use L1 normalization. The parameter cannot be used when L2 is specified. Use this parameter sparingly.

      • sgd.l2RegularizationAmount - Coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, specify a small value, such as 1.0E-04 or 1.0E-08.

        The value is a double that ranges from 0 to MAX_DOUBLE. The default is not to use L2 normalization. This parameter cannot be used when L1 is specified. Use this parameter sparingly.

      • sgd.maxPasses - The number of times that the training process traverses the observations to build the MLModel. The value is an integer that ranges from 1 to 10000. The default value is 10.

      • sgd.maxMLModelSizeInBytes - The maximum allowed size of the model. Depending on the input data, the model size might affect performance.

        The value is an integer that ranges from 100000 to 2147483648. The default value is 33554432.

    • withTrainingParameters

      public GetMLModelResult withTrainingParameters(Map<String,String> trainingParameters)

      A list of the training parameters in the MLModel. The list is implemented as a map of key/value pairs.

      The following is the current set of training parameters:

      • sgd.l1RegularizationAmount - Coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse feature set. If you use this parameter, specify a small value, such as 1.0E-04 or 1.0E-08.

        The value is a double that ranges from 0 to MAX_DOUBLE. The default is not to use L1 normalization. The parameter cannot be used when L2 is specified. Use this parameter sparingly.

      • sgd.l2RegularizationAmount - Coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, specify a small value, such as 1.0E-04 or 1.0E-08.

        The value is a double that ranges from 0 to MAX_DOUBLE. The default is not to use L2 normalization. This parameter cannot be used when L1 is specified. Use this parameter sparingly.

      • sgd.maxPasses - The number of times that the training process traverses the observations to build the MLModel. The value is an integer that ranges from 1 to 10000. The default value is 10.

      • sgd.maxMLModelSizeInBytes - The maximum allowed size of the model. Depending on the input data, the model size might affect performance.

        The value is an integer that ranges from 100000 to 2147483648. The default value is 33554432.

      Parameters:
      trainingParameters - A list of the training parameters in the MLModel. The list is implemented as a map of key/value pairs.

      The following is the current set of training parameters:

      • sgd.l1RegularizationAmount - Coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse feature set. If you use this parameter, specify a small value, such as 1.0E-04 or 1.0E-08.

        The value is a double that ranges from 0 to MAX_DOUBLE. The default is not to use L1 normalization. The parameter cannot be used when L2 is specified. Use this parameter sparingly.

      • sgd.l2RegularizationAmount - Coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, specify a small value, such as 1.0E-04 or 1.0E-08.

        The value is a double that ranges from 0 to MAX_DOUBLE. The default is not to use L2 normalization. This parameter cannot be used when L1 is specified. Use this parameter sparingly.

      • sgd.maxPasses - The number of times that the training process traverses the observations to build the MLModel. The value is an integer that ranges from 1 to 10000. The default value is 10.

      • sgd.maxMLModelSizeInBytes - The maximum allowed size of the model. Depending on the input data, the model size might affect performance.

        The value is an integer that ranges from 100000 to 2147483648. The default value is 33554432.

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

      public GetMLModelResult addTrainingParametersEntry(String key, String value)
    • clearTrainingParametersEntries

      public GetMLModelResult clearTrainingParametersEntries()
      Removes all the entries added into TrainingParameters. <p> Returns a reference to this object so that method calls can be chained together.
    • setInputDataLocationS3

      public void setInputDataLocationS3(String inputDataLocationS3)

      The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).

      Parameters:
      inputDataLocationS3 - The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
    • getInputDataLocationS3

      public String getInputDataLocationS3()

      The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).

      Returns:
      The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
    • withInputDataLocationS3

      public GetMLModelResult withInputDataLocationS3(String inputDataLocationS3)

      The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).

      Parameters:
      inputDataLocationS3 - The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
      Returns:
      Returns a reference to this object so that method calls can be chained together.
    • setMLModelType

      public void setMLModelType(String mLModelType)

      Identifies the MLModel category. The following are the available types:

      • REGRESSION -- Produces a numeric result. For example, "What listing price should a house have?"
      • BINARY -- Produces one of two possible results. For example, "Is this an e-commerce website?"
      • MULTICLASS -- Produces more than two possible results. For example, "Is this a HIGH, LOW or MEDIUM risk trade?"
      Parameters:
      mLModelType - Identifies the MLModel category. The following are the available types:

      • REGRESSION -- Produces a numeric result. For example, "What listing price should a house have?"
      • BINARY -- Produces one of two possible results. For example, "Is this an e-commerce website?"
      • MULTICLASS -- Produces more than two possible results. For example, "Is this a HIGH, LOW or MEDIUM risk trade?"
      See Also:
    • getMLModelType

      public String getMLModelType()

      Identifies the MLModel category. The following are the available types:

      • REGRESSION -- Produces a numeric result. For example, "What listing price should a house have?"
      • BINARY -- Produces one of two possible results. For example, "Is this an e-commerce website?"
      • MULTICLASS -- Produces more than two possible results. For example, "Is this a HIGH, LOW or MEDIUM risk trade?"
      Returns:
      Identifies the MLModel category. The following are the available types:

      • REGRESSION -- Produces a numeric result. For example, "What listing price should a house have?"
      • BINARY -- Produces one of two possible results. For example, "Is this an e-commerce website?"
      • MULTICLASS -- Produces more than two possible results. For example, "Is this a HIGH, LOW or MEDIUM risk trade?"
      See Also:
    • withMLModelType

      public GetMLModelResult withMLModelType(String mLModelType)

      Identifies the MLModel category. The following are the available types:

      • REGRESSION -- Produces a numeric result. For example, "What listing price should a house have?"
      • BINARY -- Produces one of two possible results. For example, "Is this an e-commerce website?"
      • MULTICLASS -- Produces more than two possible results. For example, "Is this a HIGH, LOW or MEDIUM risk trade?"
      Parameters:
      mLModelType - Identifies the MLModel category. The following are the available types:

      • REGRESSION -- Produces a numeric result. For example, "What listing price should a house have?"
      • BINARY -- Produces one of two possible results. For example, "Is this an e-commerce website?"
      • MULTICLASS -- Produces more than two possible results. For example, "Is this a HIGH, LOW or MEDIUM risk trade?"
      Returns:
      Returns a reference to this object so that method calls can be chained together.
      See Also:
    • setMLModelType

      public void setMLModelType(MLModelType mLModelType)

      Identifies the MLModel category. The following are the available types:

      • REGRESSION -- Produces a numeric result. For example, "What listing price should a house have?"
      • BINARY -- Produces one of two possible results. For example, "Is this an e-commerce website?"
      • MULTICLASS -- Produces more than two possible results. For example, "Is this a HIGH, LOW or MEDIUM risk trade?"
      Parameters:
      mLModelType - Identifies the MLModel category. The following are the available types:

      • REGRESSION -- Produces a numeric result. For example, "What listing price should a house have?"
      • BINARY -- Produces one of two possible results. For example, "Is this an e-commerce website?"
      • MULTICLASS -- Produces more than two possible results. For example, "Is this a HIGH, LOW or MEDIUM risk trade?"
      See Also:
    • withMLModelType

      public GetMLModelResult withMLModelType(MLModelType mLModelType)

      Identifies the MLModel category. The following are the available types:

      • REGRESSION -- Produces a numeric result. For example, "What listing price should a house have?"
      • BINARY -- Produces one of two possible results. For example, "Is this an e-commerce website?"
      • MULTICLASS -- Produces more than two possible results. For example, "Is this a HIGH, LOW or MEDIUM risk trade?"
      Parameters:
      mLModelType - Identifies the MLModel category. The following are the available types:

      • REGRESSION -- Produces a numeric result. For example, "What listing price should a house have?"
      • BINARY -- Produces one of two possible results. For example, "Is this an e-commerce website?"
      • MULTICLASS -- Produces more than two possible results. For example, "Is this a HIGH, LOW or MEDIUM risk trade?"
      Returns:
      Returns a reference to this object so that method calls can be chained together.
      See Also:
    • setScoreThreshold

      public void setScoreThreshold(Float scoreThreshold)

      The scoring threshold is used in binary classification MLModels, and marks the boundary between a positive prediction and a negative prediction.

      Output values greater than or equal to the threshold receive a positive result from the MLModel, such as true. Output values less than the threshold receive a negative response from the MLModel, such as false.

      Parameters:
      scoreThreshold - The scoring threshold is used in binary classification MLModels, and marks the boundary between a positive prediction and a negative prediction.

      Output values greater than or equal to the threshold receive a positive result from the MLModel, such as true. Output values less than the threshold receive a negative response from the MLModel, such as false.

    • getScoreThreshold

      public Float getScoreThreshold()

      The scoring threshold is used in binary classification MLModels, and marks the boundary between a positive prediction and a negative prediction.

      Output values greater than or equal to the threshold receive a positive result from the MLModel, such as true. Output values less than the threshold receive a negative response from the MLModel, such as false.

      Returns:
      The scoring threshold is used in binary classification MLModels, and marks the boundary between a positive prediction and a negative prediction.

      Output values greater than or equal to the threshold receive a positive result from the MLModel, such as true. Output values less than the threshold receive a negative response from the MLModel, such as false.

    • withScoreThreshold

      public GetMLModelResult withScoreThreshold(Float scoreThreshold)

      The scoring threshold is used in binary classification MLModels, and marks the boundary between a positive prediction and a negative prediction.

      Output values greater than or equal to the threshold receive a positive result from the MLModel, such as true. Output values less than the threshold receive a negative response from the MLModel, such as false.

      Parameters:
      scoreThreshold - The scoring threshold is used in binary classification MLModels, and marks the boundary between a positive prediction and a negative prediction.

      Output values greater than or equal to the threshold receive a positive result from the MLModel, such as true. Output values less than the threshold receive a negative response from the MLModel, such as false.

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

      public void setScoreThresholdLastUpdatedAt(Date scoreThresholdLastUpdatedAt)

      The time of the most recent edit to the ScoreThreshold. The time is expressed in epoch time.

      Parameters:
      scoreThresholdLastUpdatedAt - The time of the most recent edit to the ScoreThreshold. The time is expressed in epoch time.
    • getScoreThresholdLastUpdatedAt

      public Date getScoreThresholdLastUpdatedAt()

      The time of the most recent edit to the ScoreThreshold. The time is expressed in epoch time.

      Returns:
      The time of the most recent edit to the ScoreThreshold. The time is expressed in epoch time.
    • withScoreThresholdLastUpdatedAt

      public GetMLModelResult withScoreThresholdLastUpdatedAt(Date scoreThresholdLastUpdatedAt)

      The time of the most recent edit to the ScoreThreshold. The time is expressed in epoch time.

      Parameters:
      scoreThresholdLastUpdatedAt - The time of the most recent edit to the ScoreThreshold. The time is expressed in epoch time.
      Returns:
      Returns a reference to this object so that method calls can be chained together.
    • setLogUri

      public void setLogUri(String logUri)

      A link to the file that contains logs of the CreateMLModel operation.

      Parameters:
      logUri - A link to the file that contains logs of the CreateMLModel operation.
    • getLogUri

      public String getLogUri()

      A link to the file that contains logs of the CreateMLModel operation.

      Returns:
      A link to the file that contains logs of the CreateMLModel operation.
    • withLogUri

      public GetMLModelResult withLogUri(String logUri)

      A link to the file that contains logs of the CreateMLModel operation.

      Parameters:
      logUri - A link to the file that contains logs of the CreateMLModel operation.
      Returns:
      Returns a reference to this object so that method calls can be chained together.
    • setMessage

      public void setMessage(String message)

      Description of the most recent details about accessing the MLModel.

      Parameters:
      message - Description of the most recent details about accessing the MLModel.
    • getMessage

      public String getMessage()

      Description of the most recent details about accessing the MLModel.

      Returns:
      Description of the most recent details about accessing the MLModel.
    • withMessage

      public GetMLModelResult withMessage(String message)

      Description of the most recent details about accessing the MLModel.

      Parameters:
      message - Description of the most recent details about accessing the MLModel.
      Returns:
      Returns a reference to this object so that method calls can be chained together.
    • setRecipe

      public void setRecipe(String recipe)

      The recipe to use when training the MLModel. The Recipe provides detailed information about the observation data to use during training, as well as manipulations to perform on the observation data during training.

      Note

      This parameter is provided as part of the verbose format.

      Parameters:
      recipe - The recipe to use when training the MLModel. The Recipe provides detailed information about the observation data to use during training, as well as manipulations to perform on the observation data during training.

      Note

      This parameter is provided as part of the verbose format.

    • getRecipe

      public String getRecipe()

      The recipe to use when training the MLModel. The Recipe provides detailed information about the observation data to use during training, as well as manipulations to perform on the observation data during training.

      Note

      This parameter is provided as part of the verbose format.

      Returns:
      The recipe to use when training the MLModel. The Recipe provides detailed information about the observation data to use during training, as well as manipulations to perform on the observation data during training.

      Note

      This parameter is provided as part of the verbose format.

    • withRecipe

      public GetMLModelResult withRecipe(String recipe)

      The recipe to use when training the MLModel. The Recipe provides detailed information about the observation data to use during training, as well as manipulations to perform on the observation data during training.

      Note

      This parameter is provided as part of the verbose format.

      Parameters:
      recipe - The recipe to use when training the MLModel. The Recipe provides detailed information about the observation data to use during training, as well as manipulations to perform on the observation data during training.

      Note

      This parameter is provided as part of the verbose format.

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

      public void setSchema(String schema)

      The schema used by all of the data files referenced by the DataSource.

      Note

      This parameter is provided as part of the verbose format.

      Parameters:
      schema - The schema used by all of the data files referenced by the DataSource.

      Note

      This parameter is provided as part of the verbose format.

    • getSchema

      public String getSchema()

      The schema used by all of the data files referenced by the DataSource.

      Note

      This parameter is provided as part of the verbose format.

      Returns:
      The schema used by all of the data files referenced by the DataSource.

      Note

      This parameter is provided as part of the verbose format.

    • withSchema

      public GetMLModelResult withSchema(String schema)

      The schema used by all of the data files referenced by the DataSource.

      Note

      This parameter is provided as part of the verbose format.

      Parameters:
      schema - The schema used by all of the data files referenced by the DataSource.

      Note

      This parameter is provided as part of the verbose format.

      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; useful for testing and debugging.
      Overrides:
      toString in class Object
      Returns:
      A string representation of this object.
      See Also:
    • equals

      public boolean equals(Object obj)
      Overrides:
      equals in class Object
    • hashCode

      public int hashCode()
      Overrides:
      hashCode in class Object
    • clone

      public GetMLModelResult clone()
      Overrides:
      clone in class Object