  The predictive ability of database classifiers constructed with a network of
interacting chemical oscillators is studied. Databases considered here are
composed of records, where each record contains a number of parameters
(predictors) characterizing the case and the output variable describing the
case type. In a series of recent papers we have discussed the top-down design
of database classifiers, that return output information as the number of
excitations observed at a selected droplet of the network. The design is based
on an evolutionary algorithm that optimizes the network to achieve maximum
mutual information between the network evolution and the record types. Here we
discuss results illustrating that such classifiers do have a predicting
ability. They are able to give a correct classification for database records
that are not included into the process of classifier training. As example
databases we consider points inside a multidimensional unit cube and test if
they belong to a ball located at the mid of cube.
