A comparative case study on the performance of global sensitivity analysis methods on digit classification
Bibliographic record
Abstract
Global sensitivity analysis seeks to detect influential input factors contributing to a black-box model's specific decisions. This aligns with a key objective of AI explainability: Clarifying and interpreting the behavior of machine learning algorithms by identifying the features that influence their decisions-a significant approach for mitigating the computational burden associated with processing high-dimensional data. Various techniques are proposed for sensitivity analysis; however, each of these methods focuses on different mathematical aspects, which can lead to varying conclusions about the impact or importance of each feature. Therefore, it remains unclear which of these algorithms are most suitable for machine learning models and, in particular, deep learning models. Our goal is to examine the influential features identified by each sensitivity analysis algorithm and evaluate their role in helping deep learning models make accurate decisions. In this article, first, we present the mathematical foundations underlying Global Sensitivity algorithms and explain the rationale behind selecting the important features identified by each method. We then provide a comparative case study on global sensitivity analysis methods and propose a methodology to evaluate the efficacy of these methods by conducting a case study on MNIST digit dataset classification. Our study highlights the most effective global sensitivity analysis methods for detecting the key factors influencing the digit data classification.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".