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Last updated: Feb 11, 2025
Discriminant analysis makes more stringent assumptions
than logistic regression, but can be a valuable alternative or supplement to a logistic regression
analysis when those assumptions are met.
Example
node = stream.create("discriminant", "My node") node.setPropertyValue("target", "custcat") node.setPropertyValue("use_partitioned_data", False) node.setPropertyValue("method", "Stepwise")
Properties |
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field | Discriminant models require a single target field and one or more input fields. Weight and frequency fields aren't used. See Common modeling node properties for more information. |
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flag | Statistics options in the node properties under Expert Options. |
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flag | Classification options in the node properties under Expert Options. |
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number | Default value is 10. |
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flag | Matrices option Separate-groups covariance. |
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flag | Plot option Combined-groups. |
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flag | Plot option Separate-groups. |
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number | |
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number | Default value is 3.84. |
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number | Default value is 2.71. |
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number | Default value is 0.05. |
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number | Default value is 0.10. |
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