Classifier Session is the reference to the classifier session on which this VI operates.

error in (no error) describes the error status before this VI or function runs. The default is no error. If an error occurred before this VI or function runs, the VI or function passes the error in value to error out. This VI or function runs normally only if no error occurred before this VI or function runs. If an error occurs while this VI or function runs, it runs normally and sets its own error status in error out. Use the Simple Error Handler or General Error Handler VIs to display the description of the error code. Use error in and error out to check errors and to specify execution order by wiring error out from one node to error in of the next node.

	status is TRUE (X) if an error occurred before this VI or function ran or FALSE (checkmark) to indicate a warning or that no error occurred before this VI or function ran. The default is FALSE.
	code is the error or warning code. If status is TRUE, code is a nonzero error code. If status is FALSE, code is 0 or a warning code.
	source describes the origin of the error or warning and is, in most cases, the name of the VI or function that produced the error or warning. The default is an empty string.

Accuracy is the proportion of the samples in Classifier Session that are properly classified by Classifier Session.

Class Accuracy is the 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.

Classifier Session (dup) is a reference to the session referenced by Classifier Session.

Classes is the ordered list of each class in Classifier Session.

Classification Distribution is a table showing the results of the classifier session classifying every sample in Classifier Session. The first dimension corresponds to the classes assigned to samples in Classifier Session. 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.

Refer to Chapter 15, Binary Particle Classification, of the NI Vision Concepts Manual for more information about the classification distribution table, class predictive value, and class accuracy.

error out contains error information. If error in indicates that an error occurred before this VI or function ran, error out contains the same error information. Otherwise, it describes the error status that this VI or function produces. Right-click the error out indicator on the front panel and select Explain Error from the shortcut menu for more information about the error.

	status is TRUE (X) if an error occurred or FALSE (checkmark) to indicate a warning or that no error occurred.
	code is the error or warning code. If status is TRUE, code is a nonzero error code. If status is FALSE, code is 0 or a warning code.
	source describes the origin of the error or warning and is, in most cases, the name of the VI or function that produced the error or warning. The default is an empty string.

Class Predictive Value is the proportion of samples correctly classified as a given class to all samples in Classifier Session of a given class for each class in the order given in Classes.