Fisher VI
- Updated2023-02-21
- 2 minute(s) read
Fisher VI
Owning Palette: Feature Manipulation VIs
Requires: Analytics and Machine Learning Toolkit
Trains a Fisher's linear discriminant model. You can use the Fisher's linear discriminant model to reduce the dimension of training data. As a supervised model, Fisher's linear discriminant requires both healthy data and abnormal data.
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model in specifies the information about the entire workflow of the model. |
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number of components specifies the number of features after feature selection. The default is 2. |
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error in describes error conditions that occur before this node runs. This input provides standard error in functionality. |
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model out returns the information about the entire workflow of the model. Wire model out to the reference input of a standard Property Node to get an AML Analytics Property Node. |
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Fisher model info returns the information of the Fisher's linear discriminant model. Wire Fisher model info to the reference input of a standard Property Node to get an AML Fisher Property Node. |
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error out contains error information. This output provides standard error out functionality. |
Example
Refer to the Feature Manipulation (Training) VI in the labview\examples\AML\Feature Manipulation directory for an example of using the Fisher VI.