Trimming

对异常样本执行截尾处理并评估偏差(outlier 异常值)

Zafer-Liu 5052b2e 1002 B Updated

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截尾处理

先定义异常判据和业务合理范围,量化拟删除样本及其特征。仅在用户意图明确时执行,保留原始数据和可追溯输出;处理后报告样本损失及潜在选择偏差。

Tool routing

  1. Use get_schema to identify the target table and candidate numeric columns.
  2. Use profile_data to quantify outliers and candidate trim boundaries before modification.
  3. Use clean_data with the trimming operation only when the user has confirmed the rule or bounds.
  4. Use query_data after cleaning to verify row loss, boundary effects, and key metric changes.

Implementation reference

  • Tool entries: agent/tools/business/data.py::_tool_profile_data, agent/tools/business/data.py::_tool_clean_data
  • Profiling implementation: Function/Clean/data_profile.py
  • Trimming implementation: Function/Clean/trimming.py

Zafer-Liu/Data-Analysis-Agent commit 5052b2e378

Frequently asked questions

npx skillmds add zafer-liu/trimming