Applying traditional sensitivity analysis (SA) methods to machine learning (ML) models presents several challenges and limitations due to inherent differences in data structures, feature dependencies, and model complexities:
High Dimensionality: ML models often operate in high-dimensional feature spaces, which can complicate traditional SA methods that may not scale well with the number of input variables. Techniques like Sobol indices or Morris methods may become computationally expensive or infeasible as the dimensionality increases, leading to challenges in obtaining reliable sensitivity estimates.
Feature Dependencies: Many traditional SA methods assume independence among input features, which is often not the case in ML models. Feature correlations can lead to misleading sensitivity indices, as the contribution of one feature may be confounded by the presence of others. This necessitates the development of SA methods that can account for feature dependencies and interactions.
Non-linearity and Complexity: ML models, particularly ensemble methods and neural networks, exhibit non-linear relationships that traditional SA methods may struggle to capture. The complexity of these models can result in non-intuitive sensitivity results, making it difficult to interpret the influence of individual features on the model output.
Extrapolation Issues: Traditional SA methods often rely on sampling techniques that may not adequately explore the input space, especially in regions where the model has not been trained. This can lead to extrapolation errors, where the sensitivity analysis provides insights based on regions of the feature space that are not representative of the training data.
Lack of Ground Truth: In many ML applications, especially those involving complex models, there may be no clear ground truth for sensitivity measures. This lack of a reference point complicates the validation of sensitivity results and can lead to uncertainty in the interpretations drawn from the analysis.
Computational Cost: Traditional SA methods can be computationally intensive, particularly when applied to complex ML models that require extensive retraining or evaluation for each sensitivity analysis run. This can limit the practicality of applying these methods in real-time or large-scale applications.
To address these challenges, it is essential to adapt traditional SA methods to the unique characteristics of ML models, potentially developing new techniques that are specifically designed to handle high-dimensional, dependent, and complex data structures.