Skills AdvancedMLClassificationSkill

Name: AdvancedMLClassificationSkill
Author: openclaw

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。

install

source · Clone the upstream repo

git clone https://github.com/openclaw/skills

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/openclaw/skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/bamboo9805/advanced-ml-classification-skill" ~/.claude/skills/openclaw-skills-advancedmlclassificationskill && rm -rf "$T"

OpenClaw · Install into ~/.openclaw/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/openclaw/skills "$T" && mkdir -p ~/.openclaw/skills && cp -r "$T/skills/bamboo9805/advanced-ml-classification-skill" ~/.openclaw/skills/openclaw-skills-advancedmlclassificationskill && rm -rf "$T"

manifest: skills/bamboo9805/advanced-ml-classification-skill/SKILL.md

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

自动预处理（缺失值、类别编码、数值标准化）
按算法生成训练代码（优先
```
code-davinci-002
```
，失败回退本地模板）
执行算法代码并统计准确率（失败时返回具体错误）

可选交叉验证（

StratifiedKFold

KFold

RepeatedStratifiedKFold

）

可选参数搜索（
```
GridSearchCV
```
/
```
RandomizedSearchCV
```
）
生成置换特征重要性排序（默认对最佳算法）
生成新手友好中文解读（优先
```
gpt-3.5-turbo
```
）
输出可视化数据（柱状图/折线图）

运行示例

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