1. A process for modeling numerical data from a data set comprising: collecting data for development of a model with a data acquisition module; processing the data to enhance its exploitability in a data preparation module; constructing a model by learning on the processed data in a modeling module; evaluating the fit and robustness of the obtained model in a performance analysis module; adjusting the model parameters to select the optimal model in an optimization module, wherein the model is generated in the form of a D th order polynomial of the variables used in input of the modeling module, by controlling the trade-off between the learning accuracy and the learning stability with the addition to the covariance matrix of a perturbation during calculation of the model in the form of the product of a scalar &lgr; times a matrix H or in the form of a matrix H dependent on a vector of k parameters &Lgr;&equals;(&lgr; 1,&lgr; 2,... &lgr; k ) where the order d of the polynomial and the scalar &lgr;, or the vector of parameters &Lgr;, are determined automatically during model adjustment by the optimization module by integrating an additional data partition step performed by a partition module which consists in constructing two preferably disjoint subsets: a first subset comprising training data used as a learning base for the modeling module and a second subset comprising generalization data destined to adjust the value of these parameters according to a model validity criterion obtained on data that did not participate in the training, and where the matrix h is a positive defined matrix of dimensions equal to the number p of input variables into the modeling module, plus one.