pulumi-azure-native-kotlin/com.pulumi.azurenative.machinelearningservices.kotlin.outputs/TableVerticalFeaturizationSettingsResponse

TableVerticalFeaturizationSettingsResponse

data class TableVerticalFeaturizationSettingsResponse(val blockedTransformers: List<String>? = null, val columnNameAndTypes: Map<String, String>? = null, val datasetLanguage: String? = null, val enableDnnFeaturization: Boolean? = null, val mode: String? = null, val transformerParams: Map<String, List<ColumnTransformerResponse>>? = null)

Featurization Configuration.

Constructors

fun TableVerticalFeaturizationSettingsResponse(blockedTransformers: List<String>? = null, columnNameAndTypes: Map<String, String>? = null, datasetLanguage: String? = null, enableDnnFeaturization: Boolean? = null, mode: String? = null, transformerParams: Map<String, List<ColumnTransformerResponse>>? = null)

Types

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object Companion

Properties

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val blockedTransformers: List<String>? = null

These transformers shall not be used in featurization.

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val columnNameAndTypes: Map<String, String>? = null

Dictionary of column name and its type (int, float, string, datetime etc).

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val datasetLanguage: String? = null

Dataset language, useful for the text data.

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val enableDnnFeaturization: Boolean? = null

Determines whether to use Dnn based featurizers for data featurization.

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val mode: String? = null

Featurization mode - User can keep the default 'Auto' mode and AutoML will take care of necessary transformation of the data in featurization phase. If 'Off' is selected then no featurization is done. If 'Custom' is selected then user can specify additional inputs to customize how featurization is done.

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val transformerParams: Map<String, List<ColumnTransformerResponse>>? = null

User can specify additional transformers to be used along with the columns to which it would be applied and parameters for the transformer constructor.