Today, there is a growing demand for models that not only predict outcomes, but also explain the decision-making processes involved. This is particularly true in domains where decisions have significant consequences, such as in the financial and health care sectors. Whether diagnosing patients, approving loans, or managing public health initiatives, decisions influenced by machine learning methods models can have profound implications. To address this explainability issue, we show the predictive power of ensemble decision trees with the transparent logic of single trees, providing deep understanding of ensemble reasoning.