pulumi-azure-native-kotlin/com.pulumi.azurenative.machinelearningservices.kotlin.inputs/ClassificationArgs

ClassificationArgs

data class ClassificationArgs(val cvSplitColumnNames: Output<List<String>>? = null, val featurizationSettings: Output<TableVerticalFeaturizationSettingsArgs>? = null, val limitSettings: Output<TableVerticalLimitSettingsArgs>? = null, val logVerbosity: Output<Either<String, LogVerbosity>>? = null, val nCrossValidations: Output<Either<AutoNCrossValidationsArgs, CustomNCrossValidationsArgs>>? = null, val positiveLabel: Output<String>? = null, val primaryMetric: Output<Either<String, ClassificationPrimaryMetrics>>? = null, val targetColumnName: Output<String>? = null, val taskType: Output<String>, val testData: Output<MLTableJobInputArgs>? = null, val testDataSize: Output<Double>? = null, val trainingData: Output<MLTableJobInputArgs>, val trainingSettings: Output<ClassificationTrainingSettingsArgs>? = null, val validationData: Output<MLTableJobInputArgs>? = null, val validationDataSize: Output<Double>? = null, val weightColumnName: Output<String>? = null) : ConvertibleToJava<ClassificationArgs>

Classification task in AutoML Table vertical.

Constructors

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fun ClassificationArgs(cvSplitColumnNames: Output<List<String>>? = null, featurizationSettings: Output<TableVerticalFeaturizationSettingsArgs>? = null, limitSettings: Output<TableVerticalLimitSettingsArgs>? = null, logVerbosity: Output<Either<String, LogVerbosity>>? = null, nCrossValidations: Output<Either<AutoNCrossValidationsArgs, CustomNCrossValidationsArgs>>? = null, positiveLabel: Output<String>? = null, primaryMetric: Output<Either<String, ClassificationPrimaryMetrics>>? = null, targetColumnName: Output<String>? = null, taskType: Output<String>, testData: Output<MLTableJobInputArgs>? = null, testDataSize: Output<Double>? = null, trainingData: Output<MLTableJobInputArgs>, trainingSettings: Output<ClassificationTrainingSettingsArgs>? = null, validationData: Output<MLTableJobInputArgs>? = null, validationDataSize: Output<Double>? = null, weightColumnName: Output<String>? = null)

Functions

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open override fun toJava(): ClassificationArgs

Properties

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val cvSplitColumnNames: Output<List<String>>? = null

Columns to use for CVSplit data.

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val featurizationSettings: Output<TableVerticalFeaturizationSettingsArgs>? = null

Featurization inputs needed for AutoML job.

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val limitSettings: Output<TableVerticalLimitSettingsArgs>? = null

Execution constraints for AutoMLJob.

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val logVerbosity: Output<Either<String, LogVerbosity>>? = null

Log verbosity for the job.

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val nCrossValidations: Output<Either<AutoNCrossValidationsArgs, CustomNCrossValidationsArgs>>? = null

Number of cross validation folds to be applied on training dataset when validation dataset is not provided.

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val positiveLabel: Output<String>? = null

Positive label for binary metrics calculation.

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val primaryMetric: Output<Either<String, ClassificationPrimaryMetrics>>? = null

Primary metric for the task.

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val targetColumnName: Output<String>? = null

Target column name: This is prediction values column. Also known as label column name in context of classification tasks.

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val taskType: Output<String>

AutoMLJob Task type. Expected value is 'Classification'.

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val testData: Output<MLTableJobInputArgs>? = null

Test data input.

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val testDataSize: Output<Double>? = null

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.

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val trainingData: Output<MLTableJobInputArgs>

Required Training data input.

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val trainingSettings: Output<ClassificationTrainingSettingsArgs>? = null

Inputs for training phase for an AutoML Job.

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val validationData: Output<MLTableJobInputArgs>? = null

Validation data inputs.

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val validationDataSize: Output<Double>? = null

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.

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val weightColumnName: Output<String>? = null

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.