Regression Class

Definition

Namespace:

Microsoft.Azure.PowerShell.Cmdlets.MachineLearningServices.Models.Api20240401

Assembly:

Az.MachineLearningServices.private.dll

Regression task in AutoML Table vertical.

[System.ComponentModel.TypeConverter(typeof(Microsoft.Azure.PowerShell.Cmdlets.MachineLearningServices.Models.Api20240401.RegressionTypeConverter))]
public class Regression : Microsoft.Azure.PowerShell.Cmdlets.MachineLearningServices.Models.Api20240401.IRegression, Microsoft.Azure.PowerShell.Cmdlets.MachineLearningServices.Runtime.IValidates

[<System.ComponentModel.TypeConverter(typeof(Microsoft.Azure.PowerShell.Cmdlets.MachineLearningServices.Models.Api20240401.RegressionTypeConverter))>]
type Regression = class
    interface IRegression
    interface IJsonSerializable
    interface ITableVertical
    interface IAutoMlVertical
    interface IValidates

Public Class Regression
Implements IRegression, IValidates

Inheritance

Object

Regression

Attributes

TypeConverterAttribute

Implements

IAutoMlVertical IRegression ITableVertical IJsonSerializable IValidates

Constructors

Regression()

Creates an new Regression instance.

Properties

CvSplitColumnName	Columns to use for CVSplit data.
FeaturizationSetting	Featurization inputs needed for AutoML job.
FeaturizationSettingBlockedTransformer	These transformers shall not be used in featurization.
FeaturizationSettingColumnNameAndType	Dictionary of column name and its type (int, float, string, datetime etc).
FeaturizationSettingDatasetLanguage	Dataset language, useful for the text data.
FeaturizationSettingEnableDnnFeaturization	Determines whether to use Dnn based featurizers for data featurization.
FeaturizationSettingMode	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.
FeaturizationSettingTransformerParam	User can specify additional transformers to be used along with the columns to which it would be applied and parameters for the transformer constructor.
LimitSetting	Execution constraints for AutoMLJob.
LimitSettingEnableEarlyTermination	Enable early termination, determines whether or not if AutoMLJob will terminate early if there is no score improvement in last 20 iterations.
LimitSettingExitScore	Exit score for the AutoML job.
LimitSettingMaxConcurrentTrial	Maximum Concurrent iterations.
LimitSettingMaxCoresPerTrial	Max cores per iteration.
LimitSettingMaxTrial	Number of iterations.
LimitSettingTimeout	AutoML job timeout.
LimitSettingTrialTimeout	Iteration timeout.
LogVerbosity	Log verbosity for the job.
NCrossValidation	Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
NCrossValidationMode	[Required] Mode for determining N-Cross validations.
PrimaryMetric	Primary metric for regression task.
TargetColumnName	Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
TaskType	[Required] Task type for AutoMLJob.
TestData	Test data input.
TestDataDescription	Description for the input.
TestDataJobInputType	[Required] Specifies the type of job.
TestDataMode	Input Asset Delivery Mode.
TestDataSize	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.
TestDataUri	[Required] Input Asset URI.
TrainingData	[Required] Training data input.
TrainingDataDescription	Description for the input.
TrainingDataJobInputType	[Required] Specifies the type of job.
TrainingDataMode	Input Asset Delivery Mode.
TrainingDataUri	[Required] Input Asset URI.
TrainingSettingAllowedTrainingAlgorithm	Allowed models for regression task.
TrainingSettingBlockedTrainingAlgorithm	Blocked models for regression task.
TrainingSettingEnableDnnTraining	Enable recommendation of DNN models.
TrainingSettingEnableModelExplainability	Flag to turn on explainability on best model.
TrainingSettingEnableOnnxCompatibleModel	Flag for enabling onnx compatible models.
TrainingSettingEnableStackEnsemble	Enable stack ensemble run.
TrainingSettingEnableVoteEnsemble	Enable voting ensemble run.
TrainingSettingEnsembleModelDownloadTimeout	During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. Configure this parameter with a higher value than 300 secs, if more time is needed.
TrainingSettingStackEnsembleSettingStackMetaLearnerKWarg	Optional parameters to pass to the initializer of the meta-learner.
TrainingSettingStackEnsembleSettingStackMetaLearnerTrainPercentage	Specifies the proportion of the training set (when choosing train and validation type of training) to be reserved for training the meta-learner. Default value is 0.2.
TrainingSettingStackEnsembleSettingStackMetaLearnerType	The meta-learner is a model trained on the output of the individual heterogeneous models.
ValidationData	Validation data inputs.
ValidationDataDescription	Description for the input.
ValidationDataJobInputType	[Required] Specifies the type of job.
ValidationDataMode	Input Asset Delivery Mode.
ValidationDataSize	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.
ValidationDataUri	[Required] Input Asset URI.
WeightColumnName	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.

Methods

DeserializeFromDictionary(IDictionary)	Deserializes a IDictionary into an instance of Regression.
DeserializeFromPSObject(PSObject)	Deserializes a PSObject into an instance of Regression.
FromJson(JsonNode)	Deserializes a JsonNode into an instance of Microsoft.Azure.PowerShell.Cmdlets.MachineLearningServices.Models.Api20240401.IRegression.
FromJsonString(String)	Creates a new instance of Regression, deserializing the content from a json string.
ToJson(JsonObject, SerializationMode)	Serializes this instance of Regression into a JsonNode.
ToJsonString()	Serializes this instance to a json string.
ToString()
Validate(IEventListener)	Validates that this object meets the validation criteria.