Cursor Automl 编码规范

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AI工具GPT-4.1智能助手CursorAutoml

Cursor Automl 编码规范：来自 PatrickJS/awesome-cursorrules (40k stars) 的 aut，适用于各类文档与内容的智能化处理。

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Skill Documentation

Cursor Automl 编码规范

摘要

Cursor Automl 编码规范：来自 PatrickJS/awesome-cursorrules (40k stars) 的 aut，适用于各类文档与内容的智能化处理。

**文件来源:** PatrickJS/awesome-cursorrules → `rules/automl-hyperparameter-optimization.mdc`

**原仓库:** https://github.com/PatrickJS/awesome-cursorrules

**评分:** ⭐ 仓库 40k stars (社区最权威 Cursor rules 合集)

这个 skill 是干什么的?

把 `automl-hyperparameter-optimization.mdc` 这条 Cursor 编码规则打包成可调用的 AI skill,帮你把代码生成统一到一致的标准上。

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🤖 Agent 使用说明

👤 用户需要做什么?

适用场景

原始规则内容

---
description: AutoML and hyperparameter optimization rules for Python ML projects using Ray Tune, Optuna, PyCaret, and time-series AutoML libraries
globs: ["**/*.py", "**/*.ipynb", "pyproject.toml", "requirements*.txt", "environment*.yml"]
alwaysApply: false
---

# AutoML and Hyperparameter Optimization Rules

## Scope

- Use AutoML to accelerate model exploration, not to bypass problem framing, validation design, or explainability.
- Start with a simple baseline model and fixed metric before launching a search.
- Keep training, evaluation, feature generation, and search configuration separate.
- Record datasets, splits, metric definitions, random seeds, library versions, and search spaces for every run.

## Experiment Design

- Define the target metric before selecting tooling.
- Use nested validation or a final untouched test split for model selection claims.
- Use time-aware splits for time-series problems; never shuffle across time boundaries.
- Prevent leakage by fitting preprocessing only on training folds.
- Include simple baselines such as linear models, random forests, or naive time-series forecasts.
- Use early stopping and resource limits for expensive searches.
- Prefer structured search spaces with domain-informed ranges over arbitrary broad grids.

## Tooling

- Use Ray Tune or Optuna for custom training loops, distributed trials, pruning, and scheduler control.
- Use PyCaret for quick low-code comparisons when the dataset and metric are straightforward.
- Use AutoTS, Merlion, PyAF, or project-approved time-series tooling when forecast-specific validation, seasonality, and horizon handling matter.
- Store run metadata in MLflow, Weights & Biases, TensorBoard, or a project-approved tracker.
- Use `uv` or the existing project package manager for reproducible environments.

## Search Spaces

- Keep search spaces explicit and reviewed.
- Use log-scale sampling for learning rates, regularization, tree counts, and other scale-sensitive values.
- Constrain model complexity to avoid unrealistic training time or memory use.
- Include preprocessing choices only when they can be applied without leakage.
- Do not tune on the test set.

## Reporting

- Report the selected model, metric, confidence interval or variance, validation scheme, and final test result.
- Include the best parameters and the search budget.
- Compare the chosen model against the baseline and at least one non-AutoML alternative.
- Document operational constraints such as inference latency, memory use, retraining cost, and explainability.

## Common Mistakes

- Do not treat leaderboard rank as proof of production readiness.
- Do not mix train/test data during feature engineering.
- Do not run massive searches before validating labels and data quality.
- Do not ignore class imbalance, calibration, or business cost asymmetry.
- Do not deploy an AutoML model without reproducible training code and pinned dependencies.

数据来源

联系方式

有问题或建议,在本 skill 下留言。

FAQ

这个 skill 是干什么的?

把 `automl-hyperparameter-optimization.mdc` 这条 Cursor 编码规则打包成可调用的 AI skill,帮你把代码生成统一到一致的标准上。

>

👤 用户需要做什么?
  • [ ] 知道这条规则适合用在什么场景(参考下面"适用场景")
  • [ ] 把规则原文内容应用到 IDE 项目的 `.cursor/rules/` 目录(直接复制 .mdc 文件)
  • [ ] 调本 skill 时说清楚你的代码任务(语言/框架/目标)
  • [ ] 输出后人工 review 风格是否符合预期
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