Gradient Boosted Programming for Low Cardinality Classification
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Bibliographic record
Abstract
Gradient boosting represents an effective approach for constructing ensembles. We demonstrate how genetic programming can take advantage of the method for a wide range of classification tasks. The resulting Gradient Boosted Programming approach assumes two phases. Phase 1 develops a diverse set of base learners (programs). Phase 2 applies a gradient boosting approach specific to the program representation. The resulting ensemble is additively constructed and a class probability distribution is learnt for each program. An extensive benchmarking study is conducted across 21 classification datasets that include requirements for operation under class imbalance, tens of classes, and feature identification. The proposed approach is significantly better under the 11 low cardinality classification tasks and generally identifies simpler models than other ensemble methods such as Random Forests and XGBoost.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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