# classify

Classify observations using discriminant analysis

## Syntax

## Description

**Note**

`fitcdiscr`

and `predict`

are recommended over `classify`

for training a
discriminant analysis classifier and predicting labels. `fitcdiscr`

supports cross-validation and hyperparameter optimization, and does not require you to
fit the classifier every time you make a new prediction or change prior
probabilities.

classifies each row of the data in `class`

= classify(`sample`

,`training`

,`group`

)`sample`

into one of the groups to
which the data in `training`

belongs. The groups for
`training`

are specified by `group`

. The function
returns `class`

, which contains the assigned groups for each row of
`sample`

.

`[`

also returns the apparent error rate (`class`

,`err`

,`posterior`

,`logp`

,`coeff`

] = classify(___)`err`

), posterior probabilities for
training observations (`posterior`

), logarithm of the unconditional
probability density for sample observations (`logp`

), and coefficients of
the boundary curves (`coeff`

), using any of the input argument
combinations in previous syntaxes.

## Examples

## Input Arguments

## Output Arguments

## Alternative Functionality

The `fitcdiscr`

function also performs discriminant
analysis. You can train a classifier by using the `fitcdiscr`

function and
predict labels of new data by using the `predict`

function. The `fitcdiscr`

function supports
cross-validation and hyperparameter optimization, and does not require you to fit the
classifier every time you make a new prediction or change prior probabilities.

## References

[1] Krzanowski, Wojtek. J. *Principles of Multivariate
Analysis: A User's Perspective*. NY: Oxford University Press, 1988.

[2] Seber, George A. F. *Multivariate
Observations*. NJ: John Wiley & Sons, Inc., 1984.

## Version History

**Introduced before R2006a**