Do you have binary classification problem (two classes) or multi-class one?

In the first case you just have to project data on a line determined by coefficients you've found. Class #1 will be at the one side, class #2 will be at another side. You can use undocumented function DSOptimalSplit2() from

*bdss* unit which can make a split for you. Here is description of its parameters:

**Code:**

Optimal binary classification

Algorithms finds optimal (=with minimal cross-entropy) binary partition.

Internal subroutine.

INPUT PARAMETERS:

A - array[0..N-1], variable

C - array[0..N-1], class numbers (0 or 1).

N - array size

OUTPUT PARAMETERS:

Info - completetion code:

* -3, all values of A[] are same (partition is impossible)

* -2, one of C[] is incorrect (<0, >1)

* -1, incorrect pararemets were passed (N<=0).

* 1, OK

Threshold- partiton boundary. Left part contains values which are

strictly less than Threshold. Right part contains values

which are greater than or equal to Threshold.

PAL, PBL- probabilities P(0|v<Threshold) and P(1|v<Threshold)

PAR, PBR- probabilities P(0|v>=Threshold) and P(1|v>=Threshold)

CVE - cross-validation estimate of cross-entropy

-- ALGLIB --

Copyright 22.05.2008 by Bochkanov Sergey

If you have multi-class classification problem, you have to project your data at the top

*NClasses-1* eigenvectors obtained by LDA. Then you should use these values as inputs for some classification algorithm. There is no easy way to interpret such data when NClasses>2, so LDA is mostly used as preprocessing tool.