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.

Example

	model in specifies the information about the entire workflow of the model.
	number of components specifies the number of features after feature selection. The default is 2.
	error in describes error conditions that occur before this node runs. This input provides standard error in functionality.
	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.
	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.
	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.