Coverage-performance curves for classification in datasets with a typical data
Why this work is in the frame
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Bibliographic record
Abstract
Handling atypical examples in classification tasks is one of the challenges in machine learning. While there seems to be a race for accuracy, very little has been done to understand and solve the issues related to atypical data. In this paper, coverage-performance (CP) curves are introduced to help a better understanding of atypical data. The concept of CP curves is based on the idea of separating atypical data and visualizing performance of classification as a function of coverage (the fraction of data participating in training or evaluation). To generate CP curves, two schemes are compared in this paper. The first scheme is based on SVMs alone and the second one is a hybrid of a PNN and a SVM. Two generated datasets with overlapping features are used to demonstrate the effectiveness of CP curves obtained by each scheme. Calculated theoretical limits on the generated data show that the hybrid scheme is a very effective way of producing CP curves. It is also shown that by separating atypical data, although we lose some data, the performance of the classification increases significantly.
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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 | 0.001 | 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 | 0.000 | 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 it