C-SHAP: A Hybrid Method for Fast and Efficient Interpretability
Bibliographic record
Abstract
Model interpretability is essential in machine learning, particularly for applications in critical fields like healthcare, where understanding model decisions is paramount. While SHAP (SHapley Additive exPlanations) has proven to be a robust tool for explaining machine learning predictions, its high computational cost limits its practicality for real-time use. To address this, we introduce C-SHAP (Clustering-Boosted SHAP), a hybrid method that combines SHAP with K-means clustering to reduce execution times significantly while preserving interpretability. C-SHAP excels across various datasets and machine learning methods, matching SHAP’s accuracy in selected features while maintaining an accuracy of 0.73 for Random Forest with substantially faster performance. Notably, in the Diabetes dataset collected by the National Institute of Diabetes and Digestive and Kidney Diseases, C-SHAP reduces the execution time from nearly 2000 s to just 0.21 s, underscoring its potential for scalable, efficient interpretability in time-sensitive applications. Such advancements in interpretability and efficiency may hold value for enhancing decision-making within software-intensive systems, aligning with evolving engineering approaches.
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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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".