Enhancing Model Explainability with CTGAN-LIME: A Novel Approach for Interpretable Machine Learning
Bibliographic record
Abstract
Machine learning (ML) has become integral in numerous industries, offering unparalleled data analysis, pattern recognition, and predictive modelling advantages.However, the opacity of ML models, often referred to as "black boxes," poses significant challenges in understanding their decision-making processes.Explainable Artificial Intelligence (XAI) techniques aim to address this challenge by providing transparency into ML models' inner workings, enhancing human comprehension and trust.This study proposes a novel approach, CTGAN-LIME, combining Conditional Tabular Generative Adversarial Networks (CTGAN) with the LIME (Local Interpretable Model-Agnostic Explanations) framework to enhance model explainability.CTGAN-LIME addresses LIME's limitations by structuring neighbourhood sample generation and considering class balance, thereby improving the reliability and stability of explanations.Empirical evaluations across diverse datasets demonstrate CTGAN-LIME's superiority in local fidelity, stability, and local concordance over traditional LIME, underscoring its effectiveness in enhancing trustworthiness across various black-box models.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".