Estimating True and False Positive Rates in Higher Dimensional Problems and Its Data Mining Applications
Bibliographic record
Abstract
If we can estimate the accuracy of our observations then we can estimate the true and false positive rates over a series of samples in high dimensional data mining problems. To date such issues have been largely neglected and previously no algorithm has been provided to facilitate the computations involved. In high dimensional data mining tasks, increasing sparsity leads to decreasing true positive rates. Estimating this effect allows the estimation of the true size of membership of a class or cluster allowing us to identify the top candidates for these false negatives, while tracking the likelihood of false positives. These estimates of true and false positive rates can also help researchers avoid unnecessary costs by collecting only the number of samples that are really needed. We propose an algorithm for these computations designated the Statistical Error Rate Algorithm (SERA) and give an example of its use.
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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 | 0.041 | 0.176 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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".