The sample-based rule obtained from Bayes classification rule by replacing unknown parameters by ML estimates from stratified training sample is used for classification of random observations into one of two widely applicable Gamma distributions. The first order asymptotic expansions of the expected risk regret for different parametric structure cases are derived. These are used to evaluate performance of the proposed classification rule and to find the optimal training sample allocation minimizing the asymptotic expected risk regret.