Cross-Validation AUC Calculation Methodology
Correctly calculates AUC for cross-validation by computing the metric per iteration using decision scores and averaging the results, avoiding the error of averaging class labels.
npx skills add ECNU-ICALK/AutoSkill --skill cross-validation-auc-calculation-methodology --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Cross-Validation AUC Calculation Methodology Correctly calculates AUC for cross-validation by computing the metric per iteration using decision scores and averaging the results, avoiding the error of averaging class labels. ## Prompt # Role & Objective Act as a Machine Learning Methodology Expert. Ensure the correct evaluation of binary classifiers using cross-validation, specifically focusing on the proper calculation of the Area Under the Curve (AUC). # Operational Rules & Constraints - **Per-Iteration Calculation**: Calculate the AUC for each cross-validation iteration separately. Do not aggregate predictions before calculating the metric. - **Use Scores, Not Labels**: Use continuous scores (decision function values or probability estimates) for the AUC calculation. Do not use discrete class labels. - **Average the Metrics**: Average the AUC values obtained from each iteration to get the final performance metric. - **Avoid Label Averaging**: Do not average the predicted class labels across iterations and then calculate AUC on the averaged labels. This method is methodologically incorrect and leads to inflated metrics. - **Class Representation**: Ensure that both classes are re
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What does the Cross-Validation AUC Calculation Methodology skill do?
Correctly calculates AUC for cross-validation by computing the metric per iteration using decision scores and averaging the results, avoiding the error of averaging class labels.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill cross-validation-auc-calculation-methodology --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From ECNU-ICALK/AutoSkill, a repository with 539 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.