To mitigate these risks, it is crucial to establish a well-defined applicability domain (AD) for the model. One of the most commonly used approaches for defining AD is the leverage method, which is widely applied in machine learning to determine the boundaries of applicability. This method relies on two key components: leverage (h), which quantifies the influence of a given sample on the regression model, and standardized residuals, which measure the deviation between predicted and experimental values.