  This paper presents the intrinsic limit determination algorithm (ILD
Algorithm), a novel technique to determine the best possible performance,
measured in terms of the AUC (area under the ROC curve) and accuracy, that can
be obtained from a specific dataset in a binary classification problem with
categorical features {\sl regardless} of the model used. This limit, namely the
Bayes error, is completely independent of any model used and describes an
intrinsic property of the dataset. The ILD algorithm thus provides important
information regarding the prediction limits of any binary classification
algorithm when applied to the considered dataset. In this paper the algorithm
is described in detail, its entire mathematical framework is presented and the
pseudocode is given to facilitate its implementation. Finally, an example with
a real dataset is given.
