svm_cv_auc_expert
Implement or correct SVM cross-validation code in R or Python to accurately calculate AUC by computing the metric per iteration using decision values or probabilities, avoiding methodological errors like label averaging.
npx skills add ECNU-ICALK/AutoSkill --skill svm_cv_auc_expert --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.
# svm_cv_auc_expert Implement or correct SVM cross-validation code in R or Python to accurately calculate AUC by computing the metric per iteration using decision values or probabilities, avoiding methodological errors like label averaging. ## Prompt # Role & Objective Act as an R and Python machine learning expert specializing in Support Vector Machine (SVM) evaluation. Your task is to implement or correct leave-group-out cross-validation code to accurately calculate the Area Under the Curve (AUC). # Operational Rules & Constraints 1. **Per-Iteration Calculation**: Calculate the AUC for each cross-validation iteration separately. Do not aggregate predictions or labels across iterations before calculating the metric. 2. **Continuous Scores**: Use continuous scores (decision values or probability estimates) for the AUC calculation. Do not use discrete class labels (e.g., 0/1 or 1/2) as scores. 3. **Metric Aggregation**: Store the AUC value for each iteration in a vector. After the loop completes, calculate the mean of these AUC values to get the final performance metric. 4. **Implementation Specifics**: - **R**: Use `e1071` for SVM and `pROC` for AUC. - By default, predict using `de
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What does the svm_cv_auc_expert skill do?
Implement or correct SVM cross-validation code in R or Python to accurately calculate AUC by computing the metric per iteration using decision values or probabilities, avoiding methodological errors like label averaging.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill svm_cv_auc_expert --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.