Bankruptcy prediction using optimal ensemble models under balanced and imbalanced data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This study explores the performance of gradient boosting methods in bankruptcy prediction for a highly imbalanced dataset. We developed different heterogenous ensemble models based on three popular gradient boosting methods—XGBoost, LightGBM, and CatBoost. Our ensemble models were optimized using the cross‐validation method and the results of the hold‐out test sets showed that the optimized ensemble models not only outperform their base learners, but also improve the state‐of‐the‐art benchmark results on the same dataset. Interestingly, we observed that the data oversampling technique that is commonly used to address the class imbalance issue had an adverse impact on our ensemble models' performance. This indicates that our models are robust to the imbalanced dataset problem that typically degrades the classification performance of machine learning models.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 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 it