Power-controlled reliability tests for assessing probabilistic classifier calibration
Bibliographic record
Abstract
In supervised classification, a reliability diagram can be used to assess the quality of class probability estimates. Based on data binning, the reliability diagram visualizes the level of calibration of the probability outcomes. However, despite its visual appeal, it lacks statistical rigor. Efforts to add a statistical inference to a reliability diagram often encounter issues with sample size dependency, as in other statistical tests, where the statistical power increases with sample size. In this paper, we propose power-controlled reliability tests for the assessment of binary probabilistic classifiers. Our method provides consistent statistical power across different ranges of probability estimates. For each bin, the proposed method tests the reliability using a local alternative. We obtain uniform power for individual tests by allowing bins to have different sizes. Our iterative algorithm effectively determines the appropriate bin sizes as well as the total number of bins. A simulation study was conducted to investigate the performance of the proposed method across different models. We also illustrate a practical use of the proposed method with two real data sets.
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How this classification was reachedexpand
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 | 0.001 |
| 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".