Comment on “Using genomic data and machine learning to predict antibiotic resistance: A tutorial paper”
Bibliographic record
Abstract
A recent study by Faye Orcales and colleagues proposes a teaching curriculum on supervised machine learning applied to genomics data aimed at predicting antibiotic resistance. The article describes a traditional machine learning pipeline step-by-step in a way that is accessible to anyone, including novices. However, the authors provide a misleading piece of advice in the "Evaluating model performance" section, where they recommend that readers use accuracy and the F1 score for binary classification. We write this short formal comment on that article to reaffirm and explain why accuracy and the F1 score should be avoided in the evaluation of binary classification and why the Matthews correlation coefficient (MCC) should be employed instead. We also take this opportunity to warn readers about the dangers of k-fold cross-validation, which is suggested as a standard method for dividing data into training set and test set, but has several flaws and pitfalls.
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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 | 0.007 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.024 | 0.032 |
| Insufficient payload (model declined to judge) | 0.015 | 0.017 |
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".