AdvancedMLClassificationSkill
自动化生成工业级机器学习分类算法代码、调用算法做预测、输出准确率对比和可视化结果，支持新手友好的结果解读。
Best use case
AdvancedMLClassificationSkill is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
自动化生成工业级机器学习分类算法代码、调用算法做预测、输出准确率对比和可视化结果，支持新手友好的结果解读。
Teams using AdvancedMLClassificationSkill should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/advanced-ml-classification-skill/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How AdvancedMLClassificationSkill Compares
| Feature / Agent | AdvancedMLClassificationSkill | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
自动化生成工业级机器学习分类算法代码、调用算法做预测、输出准确率对比和可视化结果，支持新手友好的结果解读。
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# AdvancedMLClassificationSkill ## 输入参数 - `data_path: str`（必填）CSV 数据集路径 - `target_col: str`（必填）预测目标列名 - `algorithms: list[str]`（可选）默认 `[ "逻辑回归", "决策树", "随机森林", "XGBoost", "LightGBM" ]` - `test_size: float`（可选）默认 `0.2` - `random_state: int`（可选）默认 `42` ## 输出结构 - `accuracy_results: dict[str, float|None]` - `interpretation: str` - `generated_codes: dict[str, str]` - `visualization_data: dict` ## 关键流程 1. 自动预处理（缺失值、类别编码、数值标准化） 2. 按算法生成训练代码（优先 `code-davinci-002`，失败回退本地模板） 3. 执行算法代码并统计准确率（失败时返回具体错误） 4. 可选交叉验证（`StratifiedKFold`/`KFold`/`RepeatedStratifiedKFold`） 5. 可选参数搜索（`GridSearchCV`/`RandomizedSearchCV`） 6. 生成置换特征重要性排序（默认对最佳算法） 7. 生成新手友好中文解读（优先 `gpt-3.5-turbo`） 8. 输出可视化数据（柱状图/折线图） ## 运行示例 ```bash cd /Users/bamboo/skills/advanced-ml-classification-skill/scripts python3 -m venv .venv source .venv/bin/activate pip install -r requirements.txt python generate_complex_demo.py python advanced_ml_skill.py --data-path ./demo_complex.csv --target-col target_label --enable-cv --enable-search streamlit run app.py ```
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