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Get Classifier Accuracy (G Dataflow)

Last Modified: June 25, 2019

Provides information about the accuracy and predictive value of the trained classifier.

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classifier session in

Reference to the classifier session on which the node operates.

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error in

Error conditions that occur before this node runs.

The node responds to this input according to standard error behavior.

Standard Error Behavior

Many nodes provide an error in input and an error out output so that the node can respond to and communicate errors that occur while code is running. The value of error in specifies whether an error occurred before the node runs. Most nodes respond to values of error in in a standard, predictable way.

error in does not contain an error error in contains an error
If no error occurred before the node runs, the node begins execution normally.

If no error occurs while the node runs, it returns no error. If an error does occur while the node runs, it returns that error information as error out.

If an error occurred before the node runs, the node does not execute. Instead, it returns the error in value as error out.

Default: No error

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accuracy

Proportion of the samples in classifier session in that are properly classified by classifier session in.

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class accuracy values

Proportion of samples correctly classified as a given class to all samples classified as a given class for each class in the order given in classes.

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classifier session out

Reference to the classifier session the node creates.

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classes

Ordered list of each class in classifier session in.

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classification distribution

Table showing the results of the classifier session classifying every sample in classifier session in.

The first dimension corresponds to the classes assigned to samples in classifier session in. The second dimension corresponds to the classes into which samples are classified. Each value is the total number of samples belonging to the class corresponding to its first dimension that were classified as the class corresponding to its second dimension. The number of correctly classified samples for class X is shown in the table at the intersection of the class X first dimension and class X second dimension, along a diagonal. All other table values indicate the number of incorrectly classified samples.

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error out

Error information.

The node produces this output according to standard error behavior.

Standard Error Behavior

Many nodes provide an error in input and an error out output so that the node can respond to and communicate errors that occur while code is running. The value of error in specifies whether an error occurred before the node runs. Most nodes respond to values of error in in a standard, predictable way.

error in does not contain an error error in contains an error
If no error occurred before the node runs, the node begins execution normally.

If no error occurs while the node runs, it returns no error. If an error does occur while the node runs, it returns that error information as error out.

If an error occurred before the node runs, the node does not execute. Instead, it returns the error in value as error out.
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class predictive values

Proportion of samples correctly classified as a given class to all samples in classifier session in of a given class for each class in the order given in classes.

Where This Node Can Run:

Desktop OS: Windows

FPGA: Not supported

Web Server: Not supported in VIs that run in a web application


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