In sensitivity analysis, which aspect is primarily evaluated?

Sensitivity analysis primarily focuses on evaluating the impact of changes in input data on the outcomes of a model. This involves systematically altering the input variables to assess how those changes influence the model's predictions or results. The goal is to understand which inputs have the most significant effect on the model's behavior, helping practitioners identify which factors are the most critical and informing decisions about where to concentrate efforts in data collection or model refinement.

While overall accuracy of the model is important, it is not the primary focus of sensitivity analysis. Instead, it measures how sensitive the model's predictions are to variations in input data. Training time refers to the computational resources required to train the model and is not a consideration within the sensitivity analysis itself. Data visualization can be a tool used to illustrate the results of sensitivity analysis but does not represent what is being evaluated within the analysis. Thus, the emphasis on the impact of input data truly encapsulates the essence of sensitivity analysis.