Classification error. The plot displays the robustness of the variable selection models by summarizing the distribution of classification errors over 100 marker selection algorithms. The x-axis shows the number of markers in a model and the y-axis the classification error of the respective random forest models. The change in classification error within the repeated marker selection algorithms are shown by the gray lines. The applied model is displayed by the red line. The boxplots show summary statistics about the number of AAb in finally selected models (y-axis) and the lowest classification errors (y-axis) in the repeated models.