You are working with the text-only light edition of "H.Lohninger: Teach/Me Data Analysis, Springer-Verlag, Berlin-New York-Tokyo, 1999. ISBN 3-540-14743-8". Click here for further information.
| You are working with the text-only light edition of "H.Lohninger: Teach/Me Data Analysis, Springer-Verlag, Berlin-New York-Tokyo, 1999. ISBN 3-540-14743-8". Click here for further information.
|
Table of Contents  Multivariate Data Basic Knowledge Validation of Models PRESS |
|
| See also: cross-validation |   |
PRESS stands for "PRedictive Error Sum of Squares",
or "PREdiction Sum of Squares" 
. It is calculated
by summing all prediction errors during cross-validation. A low PRESS value
indicates a good prediction model.
The PRESS can be used to find the optimum number of components by a
stepwise variable selection procedure. The "best" model consists of as
few predictor variables as possible and shows the lowest (or almost the
lowest) PRESS. In the figure below you see an example of a hypothetical
variable selection procedure, resulting in the "best" model of 5 predictor
variables.
Note: a disadvantage of using the PRESS value is the enormous number of calculations envolved with the PRESS. This is especially true for calculation intensive models (such as neural networks) and large data sets.
Last Update: 2005-Jul-16