Bibliographic record
Abstract
Statistical models are commonly used to predict the outcome of events in a wide variety of fields such as health, finance, and business. Evaluation metrics are used to assess the effectiveness of these predictive models. One classification evaluation metric, called the receiver operating characteristics (ROC) curve has several useful properties, such as being threshold agnostic and can manage class imbalance where the outcomes are not equally represented. Despite the usefulness of the ROC curve, there is not a standard approach to extend to curve to multiclass problems. The purpose of this project was to evaluate multivariate ROC curve implementations with various underlying class proportions and degrees of separation. The methods we evaluated include the Macro, Micro, and Weighted average for one versus rest comparisons as well as the Hand and Till (HT) method. We compared the methods on simulated data with balanced, unbalanced and strongly unbalanced class proportions in combination with no separation, small separation, and large separation between classes. We found the methods were significantly different when class proportions were either unbalanced or severely unbalanced and the distributions were either separated or strongly separated (n=100, p<0.01). Pairwise comparisons found that HT and Macro were significantly different than Micro and Weighted (n=100, p<0.01). This study demonstrates that some of the AUC ROC methods differ depending on the class proportions and underlying distributions. The findings from this project may help practitioners select the most appropriate method according to their goals. Department: Computer Science Faculty Mentor: Dr. Wanhua Su
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.044 | 0.172 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".