Package com.azure.resourcemanager.machinelearning.models

Class Classification

java.lang.Object
com.azure.resourcemanager.machinelearning.models.AutoMLVertical
com.azure.resourcemanager.machinelearning.models.Classification
All Implemented Interfaces:
com.azure.json.JsonSerializable<AutoMLVertical>
public final class Classification extends AutoMLVertical
Classification task in AutoML Table vertical.

  • Constructor Details

    • Classification

      public Classification()
      Creates an instance of Classification class.

  • Method Details

    • taskType

      public TaskType taskType()
      Get the taskType property: [Required] Task type for AutoMLJob.
      Overrides:
      taskType in class AutoMLVertical
      Returns:
      the taskType value.

    • primaryMetric

      public ClassificationPrimaryMetrics primaryMetric()
      Get the primaryMetric property: Primary metric for the task.
      Returns:
      the primaryMetric value.

    • withPrimaryMetric

      public Classification withPrimaryMetric(ClassificationPrimaryMetrics primaryMetric)
      Set the primaryMetric property: Primary metric for the task.
      Parameters:
      primaryMetric - the primaryMetric value to set.
      Returns:
      the Classification object itself.

    • trainingSettings

      public ClassificationTrainingSettings trainingSettings()
      Get the trainingSettings property: Inputs for training phase for an AutoML Job.
      Returns:
      the trainingSettings value.

    • withTrainingSettings

      public Classification withTrainingSettings(ClassificationTrainingSettings trainingSettings)
      Set the trainingSettings property: Inputs for training phase for an AutoML Job.
      Parameters:
      trainingSettings - the trainingSettings value to set.
      Returns:
      the Classification object itself.

    • positiveLabel

      public String positiveLabel()
      Get the positiveLabel property: Positive label for binary metrics calculation.
      Returns:
      the positiveLabel value.

    • withPositiveLabel

      public Classification withPositiveLabel(String positiveLabel)
      Set the positiveLabel property: Positive label for binary metrics calculation.
      Parameters:
      positiveLabel - the positiveLabel value to set.
      Returns:
      the Classification object itself.

    • limitSettings

      public TableVerticalLimitSettings limitSettings()
      Get the limitSettings property: Execution constraints for AutoMLJob.
      Returns:
      the limitSettings value.

    • withLimitSettings

      public Classification withLimitSettings(TableVerticalLimitSettings limitSettings)
      Set the limitSettings property: Execution constraints for AutoMLJob.
      Parameters:
      limitSettings - the limitSettings value to set.
      Returns:
      the Classification object itself.

    • nCrossValidations

      public NCrossValidations nCrossValidations()
      Get the nCrossValidations property: Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
      Returns:
      the nCrossValidations value.

    • withNCrossValidations

      public Classification withNCrossValidations(NCrossValidations nCrossValidations)
      Set the nCrossValidations property: Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
      Parameters:
      nCrossValidations - the nCrossValidations value to set.
      Returns:
      the Classification object itself.

    • cvSplitColumnNames

      public List<String> cvSplitColumnNames()
      Get the cvSplitColumnNames property: Columns to use for CVSplit data.
      Returns:
      the cvSplitColumnNames value.

    • withCvSplitColumnNames

      public Classification withCvSplitColumnNames(List<String> cvSplitColumnNames)
      Set the cvSplitColumnNames property: Columns to use for CVSplit data.
      Parameters:
      cvSplitColumnNames - the cvSplitColumnNames value to set.
      Returns:
      the Classification object itself.

    • weightColumnName

      public String weightColumnName()
      Get the weightColumnName property: The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.
      Returns:
      the weightColumnName value.

    • withWeightColumnName

      public Classification withWeightColumnName(String weightColumnName)
      Set the weightColumnName property: The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.
      Parameters:
      weightColumnName - the weightColumnName value to set.
      Returns:
      the Classification object itself.

    • validationData

      public MLTableJobInput validationData()
      Get the validationData property: Validation data inputs.
      Returns:
      the validationData value.

    • withValidationData

      public Classification withValidationData(MLTableJobInput validationData)
      Set the validationData property: Validation data inputs.
      Parameters:
      validationData - the validationData value to set.
      Returns:
      the Classification object itself.

    • testData

      public MLTableJobInput testData()
      Get the testData property: Test data input.
      Returns:
      the testData value.

    • withTestData

      public Classification withTestData(MLTableJobInput testData)
      Set the testData property: Test data input.
      Parameters:
      testData - the testData value to set.
      Returns:
      the Classification object itself.

    • validationDataSize

      public Double validationDataSize()
      Get the validationDataSize property: The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
      Returns:
      the validationDataSize value.

    • withValidationDataSize

      public Classification withValidationDataSize(Double validationDataSize)
      Set the validationDataSize property: The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
      Parameters:
      validationDataSize - the validationDataSize value to set.
      Returns:
      the Classification object itself.

    • testDataSize

      public Double testDataSize()
      Get the testDataSize property: The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
      Returns:
      the testDataSize value.

    • withTestDataSize

      public Classification withTestDataSize(Double testDataSize)
      Set the testDataSize property: The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
      Parameters:
      testDataSize - the testDataSize value to set.
      Returns:
      the Classification object itself.

    • featurizationSettings

      public TableVerticalFeaturizationSettings featurizationSettings()
      Get the featurizationSettings property: Featurization inputs needed for AutoML job.
      Returns:
      the featurizationSettings value.

    • withFeaturizationSettings

      public Classification withFeaturizationSettings(TableVerticalFeaturizationSettings featurizationSettings)
      Set the featurizationSettings property: Featurization inputs needed for AutoML job.
      Parameters:
      featurizationSettings - the featurizationSettings value to set.
      Returns:
      the Classification object itself.

    • withLogVerbosity

      public Classification withLogVerbosity(LogVerbosity logVerbosity)
      Set the logVerbosity property: Log verbosity for the job.
      Overrides:
      withLogVerbosity in class AutoMLVertical
      Parameters:
      logVerbosity - the logVerbosity value to set.
      Returns:
      the AutoMLVertical object itself.

    • withTrainingData

      public Classification withTrainingData(MLTableJobInput trainingData)
      Set the trainingData property: [Required] Training data input.
      Overrides:
      withTrainingData in class AutoMLVertical
      Parameters:
      trainingData - the trainingData value to set.
      Returns:
      the AutoMLVertical object itself.

    • withTargetColumnName

      public Classification withTargetColumnName(String targetColumnName)
      Set the targetColumnName property: Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
      Overrides:
      withTargetColumnName in class AutoMLVertical
      Parameters:
      targetColumnName - the targetColumnName value to set.
      Returns:
      the AutoMLVertical object itself.

    • validate

      public void validate()
      Validates the instance.
      Overrides:
      validate in class AutoMLVertical
      Throws:
      IllegalArgumentException - thrown if the instance is not valid.

    • toJson

      public com.azure.json.JsonWriter toJson(com.azure.json.JsonWriter jsonWriter) throws IOException
      Specified by:
      toJson in interface com.azure.json.JsonSerializable<AutoMLVertical>
      Overrides:
      toJson in class AutoMLVertical
      Throws:
      IOException

    • fromJson

      public static Classification fromJson(com.azure.json.JsonReader jsonReader) throws IOException
      Reads an instance of Classification from the JsonReader.
      Parameters:
      jsonReader - The JsonReader being read.
      Returns:
      An instance of Classification if the JsonReader was pointing to an instance of it, or null if it was pointing to JSON null.
      Throws:
      IllegalStateException - If the deserialized JSON object was missing any required properties.
      IOException - If an error occurs while reading the Classification.