Svm Cv Auc Expert

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Five audit layers

Gates applied: no behavioural pass.

Audit 0.4.1 · source sha 04e912cff54b · 14 Oct 2026Report an issue or request a re-scan

• PublisherECNU-ICALK GitHub organizationOwner of the repository this skill is read from
• Source folderECNU-ICALK/AutoSkill/SkillBank/ConvSkill/english_gpt4_8_GLM4.7/svm_cv_auc_expertThe exact folder we audited
• LicenceNot declaredAs the repository states it
• Commit pinned for this verdict04e912cff54bA new audit runs when the source changes
• Repository statusNot archivedLast push 157 days ago
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its description: 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.

---
id: "d7b6b9ed-0808-4825-aa43-6a7a499db40e"
name: "svm_cv_auc_expert"
description: "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."
version: "0.1.2"
tags:
  - "R"
  - "Python"
  - "SVM"
  - "Cross-Validation"
  - "ROC"
  - "AUC"
triggers:
  - "SVM cross validation AUC"
  - "calculate AUC for SVM"
  - "leave group out cross validation"
  - "fix high AUC on random data"
  - "averaging classification labels"
---

# 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 `decision.values = TRUE`. Extract via `attr(pred, 'decision.values')`.
     - Only use `probability = TRUE` if explicitly requested.
     - Ensure the training set contains at least one sample from each class (e.g., `if(min(table(Y[train])) == 0) next`).
     - Suppress `pROC` warnings by setting `levels`, `direction`, or `quiet = TRUE`.
   - **Python**: Use `sklearn`. Use `decision_function` or `predict_proba` to obtain scores.
5. **Scope**: Calculate AUC using only the test set labels (`Y[test]`) and the corresponding scores for that iteration. Do not use the full label vector `Y`.

# Anti-Patterns
- Do not average decision values, probabilities, or class labels across iterations before calculating AUC.
- Do not calculate AUC on the entire dataset `Y` within a single iteration.
- Do not compute AUC on the mean of class labels.
- Do not use class labels directly as scores for ROC curves.
- Do not suggest increasing sample size or decreasing dimensions as the primary fix for AUC calculation logic errors; focus on the evaluation methodology.
- In R, do not use `probability=TRUE` by default; prefer decision values for ranking/AUC unless requested otherwise.

## Triggers

- SVM cross validation AUC
- calculate AUC for SVM
- leave group out cross validation
- fix high AUC on random data
- averaging classification labels

Excerpt, first 3,137 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

git clone https://github.com/ECNU-ICALK/AutoSkill
git -C AutoSkill checkout 04e912cff54b

Works in

1. 2026-10-14
   ✓SAFE B 89 / 100

   first audit · source 04e912cff54b

Questions about Svm Cv Auc Expert

• github.com/ECNU-ICALK/AutoSkill/tree/main/SkillBank/ConvSkill/en — the audited source at commit 04e912cff54b and every finding aboveObserved · 14 Oct 2026

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