Package com.amazonaws.services.machinelearning.model

Class GetMLModelResult

    • Constructor Detail

      • GetMLModelResult

        public GetMLModelResult()

    • Method Detail

      • 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:
        EntityStatus

      • 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:
        EntityStatus

      • 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:
        EntityStatus

      • 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:
        EntityStatus

      • 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:
        EntityStatus

      • 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.

      • 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:
        MLModelType

      • 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:
        MLModelType

      • 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:
        MLModelType

      • 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:
        MLModelType

      • 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:
        MLModelType

      • 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:
        Object.toString()

      • hashCode

        public int hashCode()
        Overrides:
        hashCode in class Object