The MDR-EFE method of performing identification of relevant factors within a given collection X_1,...,X_n is developed for stratified samples in the case of binary response variable Y. We establish a criterion of strong consistency of estimates (involving K-cross-validation procedure and penalty) for a specified prediction error function. The cost approach is proposed to compare experiments with random and nonrandom number of observations. Analytic results and simulations demonstrate advantages of the method introduced for stratified samples over that employed for i.i.d. learning sample.