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Add-SdcaNonCalibratedBinaryTrainer.md

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Add-SdcaNonCalibratedBinaryTrainer

Train a binary logistic regression classification model using the stochastic dual coordinate ascent method.

Description

Train a binary logistic regression classification model using the stochastic dual coordinate ascent method.

Syntax

Add-SdcaNonCalibratedBinaryTrainer [[-LabelColumn] <String>] [[-FeatureColumn] <String>] [[-ExampleWeightColumn] <String>] [-LossFunction <ISupportSdcaClassificationLoss>] [-L1Regularization <Nullable<Single>>] [-L2Regularization <Nullable<Single>>] [-MaxIterations <Nullable<Int32>>] [-AppendTo <EstimatorChain<ITransformer>>] [-AppendScope <TransformerScope>] [-Context <MLContext>] [<CommonParameters>]

Parameters

-LabelColumn

The name of the label column. The column data must be Boolean.

Type: System.String
Required: False
Position: 0
Default value: Label
Accept pipeline input: False
Accept wildcard characters: False

-FeatureColumn

The name of the feature column. The column data must be a known-sized vector of Single.

Type: System.String
Required: False
Position: 1
Default value: Features
Accept pipeline input: False
Accept wildcard characters: False

-ExampleWeightColumn

The name of the example weight column (optional).

Type: System.String
Required: False
Position: 2
Default value: null
Accept pipeline input: False
Accept wildcard characters: False

-LossFunction

The loss function minimized in the training process. Defaults to LogLoss if not specified.

Type: Microsoft.ML.Trainers.ISupportSdcaClassificationLoss
Required: False
Position: named
Default value: null
Accept pipeline input: False
Accept wildcard characters: False

-L1Regularization

The L1 regularization hyperparameter. Higher values will tend to lead to more sparse model.

Type: System.Single
Required: False
Position: named
Default value: null
Accept pipeline input: False
Accept wildcard characters: False

-L2Regularization

The L2 weight for regularization.

Type: System.Single
Required: False
Position: named
Default value: null
Accept pipeline input: False
Accept wildcard characters: False

-MaxIterations

The maximum number of passes to perform over the data.

Type: System.Int32
Required: False
Position: named
Default value: null
Accept pipeline input: False
Accept wildcard characters: False

-AppendTo

Append the created estimator to the end of this chain.

Type: Microsoft.ML.Data.EstimatorChain<Microsoft.ML.ITransformer>
Required: False
Position: named
Default value: null
Accept pipeline input: True (ByValue)
Accept wildcard characters: False

-AppendScope

The scope allows for 'tagging' the estimators (and subsequently transformers) in the chain to be used 'only for training', 'for training and evaluation' etc.

Type: Microsoft.ML.Data.TransformerScope
Required: False
Position: named
Default value: Everything
Accept pipeline input: False
Accept wildcard characters: False

-Context

The context on which to perform the action. If omitted, the current (cached) context will be used.

Type: Microsoft.ML.MLContext
Required: False
Position: named
Default value: Current context
Accept pipeline input: False
Accept wildcard characters: False

Common parameters

This cmdlet supports the common parameters: Verbose, Debug, ErrorAction, ErrorVariable, WarningAction, WarningVariable, OutBuffer, PipelineVariable, and OutVariable. For more information, see about_CommonParameters.

Inputs

Type Description
Microsoft.ML.Data.EstimatorChain<Microsoft.ML.ITransformer> You can pipe the EstimatorChain to append to this cmdlet.

Outputs

Type Description
Microsoft.ML.Data.EstimatorChain<Microsoft.ML.ITransformer> This cmdlet returns the appended EstimatorChain.