Classification Class

Package:
com.azure.resourcemanager.machinelearning.models
Maven Artifact:
com.azure.resourcemanager:azure-resourcemanager-machinelearning:1.1.0

public final class Classification
extends AutoMLVertical

Classification task in AutoML Table vertical.

Constructor Summary

Constructor Description
Classification()

Creates an instance of Classification class.

Method Summary

Modifier and Type Method and Description
List<String> cvSplitColumnNames()

Get the cvSplitColumnNames property: Columns to use for CVSplit data.
TableVerticalFeaturizationSettings featurizationSettings()

Get the featurizationSettings property: Featurization inputs needed for AutoML job.
static Classification fromJson(JsonReader jsonReader)

Reads an instance of Classification from the JsonReader.
TableVerticalLimitSettings limitSettings()

Get the limitSettings property: Execution constraints for AutoMLJob.
NCrossValidations nCrossValidations()

Get the nCrossValidations property: Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
String positiveLabel()

Get the positiveLabel property: Positive label for binary metrics calculation.
ClassificationPrimaryMetrics primaryMetric()

Get the primaryMetric property: Primary metric for the task.
TaskType taskType()

Get the taskType property: [Required] Task type for AutoMLJob.
MLTableJobInput testData()

Get the testData property: Test data input.
Double testDataSize()

Get the testDataSize property: The fraction of test dataset that needs to be set aside for validation purpose.
JsonWriter toJson(JsonWriter jsonWriter)
ClassificationTrainingSettings trainingSettings()

Get the trainingSettings property: Inputs for training phase for an AutoML Job.
void validate()

Validates the instance.
MLTableJobInput validationData()

Get the validationData property: Validation data inputs.
Double validationDataSize()

Get the validationDataSize property: The fraction of training dataset that needs to be set aside for validation purpose.
String weightColumnName()

Get the weightColumnName property: The name of the sample weight column.
Classification withCvSplitColumnNames(List<String> cvSplitColumnNames)

Set the cvSplitColumnNames property: Columns to use for CVSplit data.
Classification withFeaturizationSettings(TableVerticalFeaturizationSettings featurizationSettings)

Set the featurizationSettings property: Featurization inputs needed for AutoML job.
Classification withLimitSettings(TableVerticalLimitSettings limitSettings)

Set the limitSettings property: Execution constraints for AutoMLJob.
Classification withLogVerbosity(LogVerbosity logVerbosity)

Set the logVerbosity property: Log verbosity for the job.
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.
Classification withPositiveLabel(String positiveLabel)

Set the positiveLabel property: Positive label for binary metrics calculation.
Classification withPrimaryMetric(ClassificationPrimaryMetrics primaryMetric)

Set the primaryMetric property: Primary metric for the task.
Classification withTargetColumnName(String targetColumnName)

Set the targetColumnName property: Target column name: This is prediction values column.
Classification withTestData(MLTableJobInput testData)

Set the testData property: Test data input.
Classification withTestDataSize(Double testDataSize)

Set the testDataSize property: The fraction of test dataset that needs to be set aside for validation purpose.
Classification withTrainingData(MLTableJobInput trainingData)

Set the trainingData property: [Required] Training data input.
Classification withTrainingSettings(ClassificationTrainingSettings trainingSettings)

Set the trainingSettings property: Inputs for training phase for an AutoML Job.
Classification withValidationData(MLTableJobInput validationData)

Set the validationData property: Validation data inputs.
Classification withValidationDataSize(Double validationDataSize)

Set the validationDataSize property: The fraction of training dataset that needs to be set aside for validation purpose.
Classification withWeightColumnName(String weightColumnName)

Set the weightColumnName property: The name of the sample weight column.

Methods inherited from AutoMLVertical

fromJson logVerbosity targetColumnName taskType toJson trainingData validate withLogVerbosity withTargetColumnName withTrainingData

Methods inherited from java.lang.Object

clone equals finalize getClass hashCode notify notifyAll toString wait wait wait

Constructor Details

Classification

public Classification()

Creates an instance of Classification class.

Method Details

cvSplitColumnNames

public List<String> cvSplitColumnNames()

Get the cvSplitColumnNames property: Columns to use for CVSplit data.

Returns:

the cvSplitColumnNames value.

featurizationSettings

public TableVerticalFeaturizationSettings featurizationSettings()

Get the featurizationSettings property: Featurization inputs needed for AutoML job.

Returns:

the featurizationSettings value.

fromJson

public static Classification fromJson(JsonReader jsonReader)

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:

IOException

 - If the deserialized JSON object was missing any required properties.

limitSettings

public TableVerticalLimitSettings limitSettings()

Get the limitSettings property: Execution constraints for AutoMLJob.

Returns:

the limitSettings value.

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.

positiveLabel

public String positiveLabel()

Get the positiveLabel property: Positive label for binary metrics calculation.

Returns:

the positiveLabel value.

primaryMetric

public ClassificationPrimaryMetrics primaryMetric()

Get the primaryMetric property: Primary metric for the task.

Returns:

the primaryMetric value.

taskType

public TaskType taskType()

Get the taskType property: [Required] Task type for AutoMLJob.

Overrides:

Classification.taskType()

Returns:

the taskType value.

testData

public MLTableJobInput testData()

Get the testData property: Test data input.

Returns:

the testData value.

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.

toJson

public JsonWriter toJson(JsonWriter jsonWriter)

Overrides:

Classification.toJson(JsonWriter jsonWriter)

Parameters:

jsonWriter

Throws:

trainingSettings

public ClassificationTrainingSettings trainingSettings()

Get the trainingSettings property: Inputs for training phase for an AutoML Job.

Returns:

the trainingSettings value.

validate

public void validate()

Validates the instance.

Overrides:

Classification.validate()

validationData

public MLTableJobInput validationData()

Get the validationData property: Validation data inputs.

Returns:

the validationData value.

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.

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.

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.

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.

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.

withLogVerbosity

public Classification withLogVerbosity(LogVerbosity logVerbosity)

Set the logVerbosity property: Log verbosity for the job.

Overrides:

Classification.withLogVerbosity(LogVerbosity logVerbosity)

Parameters:

logVerbosity

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.

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.

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.

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:

Classification.withTargetColumnName(String targetColumnName)

Parameters:

targetColumnName

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.

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.

withTrainingData

public Classification withTrainingData(MLTableJobInput trainingData)

Set the trainingData property: [Required] Training data input.

Overrides:

Classification.withTrainingData(MLTableJobInput trainingData)

Parameters:

trainingData

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.

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.

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.

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.

Applies to