  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.
