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

public class MLModel extends Object implements Serializable, Cloneable

Represents the output of a GetMLModel operation.

The content consists of the detailed metadata and the current status of the MLModel.

See Also:
  • Constructor Details

    • MLModel

      public MLModel()
  • Method Details

    • setMLModelId

      public void setMLModelId(String mLModelId)

      The ID assigned to the MLModel at creation.

      Parameters:
      mLModelId - The ID assigned to the MLModel at creation.
    • getMLModelId

      public String getMLModelId()

      The ID assigned to the MLModel at creation.

      Returns:
      The ID assigned to the MLModel at creation.
    • withMLModelId

      public MLModel withMLModelId(String mLModelId)

      The ID assigned to the MLModel at creation.

      Parameters:
      mLModelId - The ID assigned to the MLModel at creation.
      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. The CreateMLModel operation uses the TrainingDataSourceId.

      Parameters:
      trainingDataSourceId - The ID of the training DataSource. The CreateMLModel operation uses the TrainingDataSourceId.
    • getTrainingDataSourceId

      public String getTrainingDataSourceId()

      The ID of the training DataSource. The CreateMLModel operation uses the TrainingDataSourceId.

      Returns:
      The ID of the training DataSource. The CreateMLModel operation uses the TrainingDataSourceId.
    • withTrainingDataSourceId

      public MLModel withTrainingDataSourceId(String trainingDataSourceId)

      The ID of the training DataSource. The CreateMLModel operation uses the TrainingDataSourceId.

      Parameters:
      trainingDataSourceId - The ID of the training DataSource. The CreateMLModel operation uses the TrainingDataSourceId.
      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 MLModel 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 MLModel 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 MLModel 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 MLModel 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 an MLModel. This element can have one of the following values:

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

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

      public String getStatus()

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

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

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

      public MLModel withStatus(String status)

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

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

      • PENDING - Amazon Machine Learning (Amazon ML) submitted a request to create an MLModel.
      • INPROGRESS - The creation process is underway.
      • FAILED - The request to create an MLModel did not run to completion. It is not usable.
      • COMPLETED - The creation process 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 an MLModel. This element can have one of the following values:

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

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

      public MLModel withStatus(EntityStatus status)

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

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

      • PENDING - Amazon Machine Learning (Amazon ML) submitted a request to create an MLModel.
      • INPROGRESS - The creation process is underway.
      • FAILED - The request to create an MLModel did not run to completion. It is not usable.
      • COMPLETED - The creation process 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 MLModel 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 MLModel 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 valus is a double that ranges from 0 to MAX_DOUBLE. The default is not to use L2 normalization. This cannot be used when L1 is specified. Use this parameter sparingly.

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

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

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

      • sgd.maxPasses - 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 - 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 MLModel 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 valus is a double that ranges from 0 to MAX_DOUBLE. The default is not to use L2 normalization. This cannot be used when L1 is specified. Use this parameter sparingly.

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

      • sgd.maxPasses - 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 - 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 MLModel addTrainingParametersEntry(String key, String value)
    • clearTrainingParametersEntries

      public MLModel 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 MLModel 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.
    • setAlgorithm

      public void setAlgorithm(String algorithm)

      The algorithm used to train the MLModel. The following algorithm is supported:

      • SGD -- Stochastic gradient descent. The goal of SGD is to minimize the gradient of the loss function.
      Parameters:
      algorithm - The algorithm used to train the MLModel. The following algorithm is supported:

      • SGD -- Stochastic gradient descent. The goal of SGD is to minimize the gradient of the loss function.
      See Also:
    • getAlgorithm

      public String getAlgorithm()

      The algorithm used to train the MLModel. The following algorithm is supported:

      • SGD -- Stochastic gradient descent. The goal of SGD is to minimize the gradient of the loss function.
      Returns:
      The algorithm used to train the MLModel. The following algorithm is supported:

      • SGD -- Stochastic gradient descent. The goal of SGD is to minimize the gradient of the loss function.
      See Also:
    • withAlgorithm

      public MLModel withAlgorithm(String algorithm)

      The algorithm used to train the MLModel. The following algorithm is supported:

      • SGD -- Stochastic gradient descent. The goal of SGD is to minimize the gradient of the loss function.
      Parameters:
      algorithm - The algorithm used to train the MLModel. The following algorithm is supported:

      • SGD -- Stochastic gradient descent. The goal of SGD is to minimize the gradient of the loss function.
      Returns:
      Returns a reference to this object so that method calls can be chained together.
      See Also:
    • setAlgorithm

      public void setAlgorithm(Algorithm algorithm)

      The algorithm used to train the MLModel. The following algorithm is supported:

      • SGD -- Stochastic gradient descent. The goal of SGD is to minimize the gradient of the loss function.
      Parameters:
      algorithm - The algorithm used to train the MLModel. The following algorithm is supported:

      • SGD -- Stochastic gradient descent. The goal of SGD is to minimize the gradient of the loss function.
      See Also:
    • withAlgorithm

      public MLModel withAlgorithm(Algorithm algorithm)

      The algorithm used to train the MLModel. The following algorithm is supported:

      • SGD -- Stochastic gradient descent. The goal of SGD is to minimize the gradient of the loss function.
      Parameters:
      algorithm - The algorithm used to train the MLModel. The following algorithm is supported:

      • SGD -- Stochastic gradient descent. The goal of SGD is to minimize the gradient of the loss function.
      Returns:
      Returns a reference to this object so that method calls can be chained together.
      See Also:
    • 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 a child-friendly web site?".
      • 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 a child-friendly web site?".
      • 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 a child-friendly web site?".
      • 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 a child-friendly web site?".
      • MULTICLASS - Produces more than two possible results. For example, "Is this a HIGH, LOW or MEDIUM risk trade?".
      See Also:
    • withMLModelType

      public MLModel 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 a child-friendly web site?".
      • 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 a child-friendly web site?".
      • 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 a child-friendly web site?".
      • 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 a child-friendly web site?".
      • MULTICLASS - Produces more than two possible results. For example, "Is this a HIGH, LOW or MEDIUM risk trade?".
      See Also:
    • withMLModelType

      public MLModel 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 a child-friendly web site?".
      • 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 a child-friendly web site?".
      • 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)
      Parameters:
      scoreThreshold -
    • getScoreThreshold

      public Float getScoreThreshold()
      Returns:
    • withScoreThreshold

      public MLModel withScoreThreshold(Float scoreThreshold)
      Parameters:
      scoreThreshold -
      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 MLModel 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.
    • setMessage

      public void setMessage(String message)

      A description of the most recent details about accessing the MLModel.

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

      public String getMessage()

      A description of the most recent details about accessing the MLModel.

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

      public MLModel withMessage(String message)

      A description of the most recent details about accessing the MLModel.

      Parameters:
      message - A 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.
    • 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 MLModel clone()
      Overrides:
      clone in class Object