FAQ  Frequently Asked Questions
Issue:

How do I interpret the Misclassification results reported by crossval?
Possible Solutions:

The Crossval function (used for crossvalidation) allows "discriminant analysis" in which a yvector or matrix is supplied which indicates which samples are in one or more classes. This yblock is usually a logical (boolean) array where each column represents one classes membership. A value of 1 in a column indicates that the given sample is a member of that column's class. A value of 0 indicates that sample is not a member of the class.
The report at the end of crossval provides a tabular description of the results each column. In these tables, the numbers represent "misclassification rates". These are fractional errors of classification where 0 indicates that no samples in the given group were misclassified and 1 indicates that all samples in the given group were misclassified.
Specifically, the groups in each table are usually labeled as "class 0" and "class 1" (see below for an example). Class 0 represents the group of samples which were labeled 0 ("notinclass") for the given column. Class 1 represents the group of samples which were labeled 1 ("inclass"). As such, the misclassification results for Class 0 can be interpreted as falsepostive rates and the misclassification results for Class 1 can be interpreted as falsenegative rates.
In the example below, the false positive rate for 3 latent variables (components) is 0.076 = 7.6% false positive rate. The false negative rate at 3 latent variables is 0.000, or 0 false negatives (perfect classification).
Fractional Misclassification (Ycolumn 1) Class # Comp # 0 1    1 0.530 0.556 2 0.258 0.111 3 0.076 0.000
Note that these false positive and false negative rates can be easily used to calculate sensitivity and specificity using the relationship:
specificity = 1  (false positive rate) sensitivity = 1  (false negative rate)
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