Symbolic or explainable learning models stand out within the Machine Learning area because they are self-explanatory, making the decision process easier to be interpreted by humans. However, these models are overly responsive to the training set used. Thus, even tiny variations in training sets can result in much worse precision. In this research we propose a meta-learning approach that transforms a Random Forest into a single Decision Tree. Experiments were performed on classification datasets from different domains. Our approach using precision (positive reliability) performs as good as a Random Forest with no statistically significant differences. Yet, its advantage is the interpretability provided by a single decision tree. Results indicate that it is possible to obtain a resulting model which is easier to interpret than a Random Forest, still with higher precision than a standard Decision Tree.