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ml-data-preprocessing

用于数据清洗、归一化和防止数据泄露的安全数据集准备的机器学习数据预处理工作流程。当训练/推理数据管道需要明确的预处理决策时使用;不要将其用于通用API层或仅基础设施的变化。

person作者: jakexiaohubgithub

Ml Data Preprocessing

Overview

Use this skill to define preprocessing that improves model quality without introducing leakage or unreproducible transforms.

Scope Boundaries

  • Use this skill when the task matches the trigger condition described in description.
  • Do not use this skill when the primary task falls outside this skill's domain.

Shared References

  • Leakage prevention rules:
    • references/leakage-prevention-rules.md

Templates And Assets

  • Preprocessing spec template:
    • assets/preprocessing-spec-template.md

Inputs To Gather

  • Source datasets, schema quality, and time boundaries.
  • Missing/outlier characteristics and domain constraints.
  • Train/validation/test split policy.
  • Reproducibility and compliance requirements.

Deliverables

  • Preprocessing specification with transformation rationale.
  • Leakage and data-quality validation plan.
  • Reproducibility notes and versioning requirements.

Workflow

  1. Draft transform plan with assets/preprocessing-spec-template.md.
  2. Validate temporal and label safety via references/leakage-prevention-rules.md.
  3. Define split-safe transformations and quality checks.
  4. Verify transform repeatability across runs.
  5. Publish preprocessing contract and residual risks.

Quality Standard

  • Transformations are deterministic and versioned.
  • Leakage risk is explicitly checked and mitigated.
  • Data loss/quality trade-offs are documented.

Failure Conditions

  • Stop when preprocessing introduces label or temporal leakage.
  • Stop when transforms are not reproducible.
  • Escalate when data quality blocks decision-grade training.