## FAQ - Frequently Asked Questions

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### Issue:

How do I interpret the Misclassification results reported by crossval?

### Possible Solutions:

The Crossval function (used for cross-validation) allows "discriminant analysis" in which a y-vector or matrix is supplied which indicates which samples are in one or more classes. This y-block 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 mis-classified and 1 indicates that all samples in the given group were mis-classified.

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 ("not-in-class") for the given column. Class 1 represents the group of samples which were labeled 1 ("in-class"). As such, the misclassification results for Class 0 can be interpreted as false-postive rates and the misclassification results for Class 1 can be interpreted as false-negative 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 (Y-column 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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