AutoMLVerticalRegression Class

Definition

Namespace:

Azure.ResourceManager.MachineLearning.Models

Assembly:

Azure.ResourceManager.MachineLearning.dll

Package:

Azure.ResourceManager.MachineLearning v1.1.1

Source:

AutoMLVerticalRegression.cs

Regression task in AutoML Table vertical.

public class AutoMLVerticalRegression : Azure.ResourceManager.MachineLearning.Models.AutoMLVertical

type AutoMLVerticalRegression = class
    inherit AutoMLVertical

Public Class AutoMLVerticalRegression
Inherits AutoMLVertical

Inheritance

Object

AutoMLVertical

AutoMLVerticalRegression

Constructors

AutoMLVerticalRegression(MachineLearningTableJobInput)

Initializes a new instance of AutoMLVerticalRegression.

Properties

CvSplitColumnNames	Columns to use for CVSplit data.
FeaturizationSettings	Featurization inputs needed for AutoML job.
LimitSettings	Execution constraints for AutoMLJob.
LogVerbosity	Log verbosity for the job. (Inherited from AutoMLVertical)
NCrossValidations	Number of cross validation folds to be applied on training dataset when validation dataset is not provided. Please note NCrossValidations is the base class. According to the scenario, a derived class of the base class might need to be assigned here, or this property needs to be casted to one of the possible derived classes. The available derived classes include AutoNCrossValidations and CustomNCrossValidations.
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. (Inherited from AutoMLVertical)
TestData	Test data input.
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.
TrainingData	[Required] Training data input. (Inherited from AutoMLVertical)
TrainingSettings	Inputs for training phase for an AutoML Job.
ValidationData	Validation data inputs.
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.
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.