1. A method for automatically analyzing data and constructing data classification models based on the data, the method comprising: selecting a best combination of methods from a plurality of classification, predictor selection, and data preparatory methods; determining a best model that corresponds to one or more best parameters of the classification, predictor selection, and data preparatory methods for the data to be analyzed; estimating the performance of the best model using new data that was not used in selecting the best combination of methods or in determining the best model; returning a small set of predictors sufficient for the classification task; and performing optimal model selection and estimating performance of the data classification models using the N non-overlapping and balanced subsets and a nested cross-validation procedure.