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The SVD algorithm supports the following settings:
Approximate Computation: Specifies whether the algorithm should use approximate computations to improve performance. For SVD, approximation is often appropriate for data sets with many columns. An approximate low-rank decomposition provides good solutions at a reasonable computational cost. If you disable approximate computations for SVD, approximation is dependent on the characteristics of the data. For data sets with more than 2500 attributes (the maximum number of features allowed) only approximate decomposition is possible. If approximate computation is disabled for a data set with more than 2500 attributes, an exception is raised.
Values for Approximate Computation are
System Determined, the default
Enable
Disable
Automatic Preparation on or off. Default is on.
Number of Features default is system determined. You can specify a number.
Scoring Mode is the scoring mode to use, either Singular Value Decomposition Scoring or Principal Components Analysis Scoring. Default is Singular Value Decomposition Scoring (SVD scoring). When the build data is scored with SVD, the projections will be the same as the U matrix. When the build data is scored with PCA, the projections will be the product of the U and S matrices.
U Matrix Output Whether or not to persist the U matrix produced by SVD. The U matrix in SVD has as many rows as the number of rows in the build data. To avoid creating a large model, the U matrix is persisted only when U Matrix Output is enabled. When U Matrix Output is enabled, the build data must include a Case ID. Default is Disable.