Honest assessments of automatic learning algorithm performance.
Bibliographic record
Abstract
OBJECTIVE: To compare methods of evaluating probabilistic predictors in systems that learn from examples. STUDY DESIGN: The performance of four automatic learning algorithms, representing current machine learning technology, were assessed using four methodologies in the task of separating normal squamous intermediate cervical cells from all other segmented objects in digital images. Two of the methodologies were carefully constructed to model sources of variation associated with the choice of training and test sets. These assessments were statistically compared with assessments using both standard and a modified version of cross-validation. RESULTS: The investigation illustrates the tradeoffs involved in obtaining statistical rigor as compared with the cost of collecting data. While cross-validation makes frugal use of data, it can produce misleading assessments of algorithm performance in terms of both bias and variance. The modified version produces more reliable assessments but in some cases may also be misleading. CONCLUSION: We suggest that users of learning algorithms should exercise judicious care in evaluating learning algorithm performance in order to avoid unnecessary bias and large variance in their assessments.
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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 | 0.135 | 0.445 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".