In this study, the error-adjusted bagging technique is adapted to support vector regression (SVR) and regression tree (RT) methods to obtain more accurate predictions, and then the method performances are evaluated with real data sets and a simulation study. For this purpose, the prediction performances of single models, classical bagging models, and error-adjusted bagging models obtained via complementary versions of the above-mentioned methods are constructed. The comparison is mainly based on a real dataset of 295 patients with Hodgkin's lymphoma (HL). The effect of several parameters such as training set ratio, the number of influential predictors on model performances, is examined with 500 repetitions of simulation data. The results reveal that error-adjusted bagging method provides the best performance compared to both single and classical bagging performances of the methods. Furthermore, the bias variance analysis confirms the success of this technique in reducing both bias and variance.