Prediction Performance Metrics Considering the Difficulty of Individual Cases
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Prediction performance evaluation is an essential step in machine learning model development. Model performance is generally assessed based on the number of correct and incorrect predictions it makes. However, this evaluation metric has a limitation in that it treats all cases equally, regardless of their varying levels of prediction difficulty. In this paper, we propose novel prediction performance metrics considering the prediction difficulty. The novel performance metrics reward models for correct predictions on difficult cases and penalize them for incorrect predictions on easy cases. The prediction difficulty of individual cases is measured using three case difficulty calculation metrics developed by neural networks. We conducted experiments using a variety of datasets and seven machine learning models to compare prediction performance with and without considering the difficulty of individual cases. The experimental results demonstrate that our novel prediction performance metrics enhance the understanding of model performance from various aspects and provide a more detailed explanation of model performance than conventional performance metrics.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.000 | 0.002 |
| 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