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Ecnu Icalk/回归模型评估与网格搜索调参 | DecimalAI

ai mlby ecnu-icalk

Ecnu Icalk/回归模型评估与网格搜索调参

ecnu-icalk/回归模型评估与网格搜索调参

Source ecnu-icalk/autoskill ↗License Not recorded

提供使用R^2指标评估线性回归、支持向量回归(SVR)和随机森林模型的代码，并演示如何使用网格搜索和交叉验证进行参数调优。

SkillScore100Maintained

Automatic

AI Engineer pack

Last verified 8/18/2026

What it changes

Passed 22 of 22 test cases with the skill, and 19 of 22 without it.

Without skill86%

With skill100%

Checks it passed that the model alone missed

The evaluation score printed inside the loop is R^2.
The random forest tuning is executed via GridSearchCV.
The script instantiates LinearRegression, SVR, and RandomForestRegressor.

3 fixed19 passed either way0 still failing0 broke

Pass-rate lift: +14 pp
Tokens: +2%
Agent turns: 0%
Response time: -16%

Model gemini-3.6-flashMeasured 8/18/2026v122 of 22 cases passed with skill

Tokens, turns and time describe resource use, not a dollar price.

Use this skill

Save it into your project's skills folder. Claude Code picks it up in its next session.

curl -fsSL --create-dirs -o .claude/skills/ecnu-icalk-3d5894/SKILL.md https://app.decimal.ai/s/ecnu-icalk-2c146b/SKILL.md

Want it pinned and updatable? pip install "decimalai>=0.15", then decimalai skills add ecnu-icalk-2c146b, and start Claude Code with claude --plugin-dir "$(git rev-parse --show-toplevel)/.agents"

1 · One-time — install the CLI (docs ↗)

pip install "decimalai>=0.15"

2 · Get the skill — writes .agents/skills/ecnu-icalk-3d5894/ and pins it in .decimal/skills.lock

decimalai skills add ecnu-icalk-2c146b

No CLI? Get the same file — curl · raw file

curl -fsSL --create-dirs -o .claude/skills/ecnu-icalk-3d5894/SKILL.md https://app.decimal.ai/s/ecnu-icalk-2c146b/SKILL.md

Raw file: /s/ecnu-icalk-2c146b/SKILL.md ↗ · machine-readable summary at /s/ecnu-icalk-2c146b.json ↗

Safety checks: 3 of 3 passed

Code scan CleanIntent PassedContent Clean

See it in action

One of 3 test cases the skill fixed. Same model, same prompt, with and without the skill.

1 of 3

Prompt

我需要编写一段Python代码来对比三种经典表格回归算法（线性模型、核函数方法和树集成方法）。请演示完整的数据划分、训练、预测及评估流程。

What was checked, without and with the skill
Checked	Without skill	With skill
The script instantiates LinearRegression, SVR, and RandomForestRegressor.	Missed	Passed
The metrics output evaluates performance using R^2 score.	Passed	Passed

case-06 · gemini-3.6-flash · 8/18/2026 · answers are capped and redacted

What's inside

The instructions your model reads when the skill loads.

SKILL.md31 linessingle file

回归模型评估与网格搜索调参

提供使用R^2指标评估线性回归、支持向量回归(SVR)和随机森林模型的代码，并演示如何使用网格搜索和交叉验证进行参数调优。

Prompt

Role & Objective

你是一个机器学习编程助手。你的任务是根据用户需求，提供使用Python的sklearn库对线性回归、支持向量回归(SVR)和随机森林回归模型进行评估和参数调优的代码。

Operational Rules & Constraints

模型选择：主要涉及 LinearRegression、SVR 和 RandomForestRegressor。
评估指标：默认使用 R^2 (决定系数) 作为评估指标，使用 r2_score 或模型的 .score() 方法。
参数调优：当用户询问参数设置或调优时，必须使用 GridSearchCV 结合交叉验证（如 cv=5）来寻找最佳参数组合。
代码结构：代码应包含数据划分（train_test_split）、模型定义、模型训练、预测及评分步骤。
SVR调参：针对SVR，重点展示 kernel、C 和 gamma 参数的网格搜索设置。

Communication & Style Preferences

代码需清晰、可运行，并包含必要的注释。
解释参数含义时，结合其对模型拟合程度（过拟合/欠拟合）的影响。

Anti-Patterns

不要仅提供理论解释而不提供代码。
不要使用未在用户要求中提及的复杂模型或评估指标，除非为了对比。

Triggers

评估线性回归、支持向量回归和随机森林
回归模型网格搜索调参
SVR参数调优
使用R^2评估模型性能
如何找到回归模型最佳参数

Track record

Benchmark

22 / 22

test cases passed, vs 19 / 22 without

Adoption

Users: 0
Times used: 0

Safety and source details

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 8/3/2026

View 15 scanner checks

live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

ecnu-icalk / autoskill

SkillBank/ConvSkill/chinese_gpt3.5_8/%E5%9B%9E%E5%BD%92%E6%A8%A1%E5%9E%8B%E8%AF%84%E4%BC%B0%E4%B8%8E%E7%BD%91%E6%A0%BC%E6%90%9C%E7%B4%A2%E8%B0%83%E5%8F%82/SKILL.md

View on GitHub ↗

Imported 8/3/2026Version v1

More skills from the same source and nearby in the registry, with their headline benchmark lift.

Ready to supercharge your AI agents?

See it in action

What's inside

回归模型评估与网格搜索调参

Prompt

Role & Objective

Operational Rules & Constraints

Communication & Style Preferences

Anti-Patterns

Triggers

Track record

Benchmark

Adoption

SkillSafety

Source

Related skills