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Add-RandomizedPcaTrainer.md

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Add-RandomizedPcaTrainer

Train an approximate PCA using Randomized SVD algorithm.

Description

Train an approximate PCA using Randomized SVD algorithm.

Syntax

Add-RandomizedPcaTrainer [[-FeatureColumn] <String>] [[-ExampleWeightColumn] <String>] [-Rank <Int32>] [-Oversampling <Int32>] [-DontEnsureZeroMean] [-Seed <Nullable<Int32>>] [-AppendTo <EstimatorChain<ITransformer>>] [-AppendScope <TransformerScope>] [-Context <MLContext>] [<CommonParameters>]

Parameters

-FeatureColumn

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

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

-ExampleWeightColumn

The name of the example weight column (optional). To use the weight column, the column data must be of type Single.

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

-Rank

The number of components in the PCA.

Type: System.Int32
Required: False
Position: named
Default value: 20
Accept pipeline input: False
Accept wildcard characters: False

-Oversampling

Oversampling parameter for randomized PCA training.

Type: System.Int32
Required: False
Position: named
Default value: 20
Accept pipeline input: False
Accept wildcard characters: False

-DontEnsureZeroMean

If enabled, data is not centered to be zero mean.

Type: System.Management.Automation.SwitchParameter
Required: False
Position: named
Default value: False
Accept pipeline input: False
Accept wildcard characters: False

-Seed

The seed for random number generation.

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.