Sensitivity analysis is a method used to understand how the variation in the output of a predictive model can be attributed to different sources of variation in its input variables. By systematically changing one or more input variables within specific ranges while keeping others constant, sensitivity analysis helps identify which inputs have the most influence on the model’s outcomes. This technique is essential for evaluating the robustness and reliability of models, improving decision-making, and managing uncertainty. Sensitivity analysis is widely used across various fields such as finance, engineering, environmental science, and economics to assess how sensitive a model’s predictions are to variations in parameters and to prioritize data collection and further analysis.
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