Recently, machine learning and deep learning technologies have been successfully employed in many complicated high-stake decision-making applications such as disease prediction, fraud detection, outlier detection, and criminal justice sentencing. All these applications share a common trait known as risk-aversion in economics and finance terminologies. In other words, the decision-makers tend to have an extremely low risk tolerance. Under this context, decision-making parameters will significantly affect the performance of models. For example, in binary classification problems, we use the so-called classification threshold as the decision parameter. In the following examples, we see that changing the threshold leads to significantly different model performances.