Regression insight

Skill serejaris/kimi-skills/skills/regression-insight

Полная коллекция скиллов Kimi (267 built-in + 7 plugin skills), выгруженная из сандбокса агента

Install

npx -y skills add serejaris/kimi-skills --skill regression-insight

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What its author says it does

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对 CSV/Excel 数据执行线性回归（OLS）或逻辑回归（Logistic），一键输出完整统计结果（包含回归系数、R²、p值、VIF等）和中文通俗解读。当用户提及回归分析、拟合模型、查看系数显著性、R方、p值、共线性（VIF），或使用关键词如回归、regression、OLS、logit、拟合、显著性时触发。

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SKILL.md

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regression-analyzer

自动回归建模工具 —— 对表格数据执行线性回归（OLS）或逻辑回归（Logit），一键输出完整统计结果和中文通俗解读。

能力概览

功能	说明
线性回归	OLS，输出系数、R²、调整 R²、F 检验、AIC/BIC、Durbin-Watson
逻辑回归	Logit，输出系数、Odds Ratio、Pseudo R²、似然比检验
多重共线性检测	每个自变量的 VIF 值 + 警告级别
通俗解读	用中文对每个指标和系数给出"什么意思/该怎么看"的说明
自动检测	目标变量为 0/1 时自动切换逻辑回归

Quick Start

# 线性回归：预测 price，用所有数值列做自变量
python3 scripts/regression_analyzer.py data.csv --target price

# 逻辑回归：预测 churn（0/1），指定特征列
python3 scripts/regression_analyzer.py users.csv --target churn --features "age,income,tenure"

# 保存结果到 JSON
python3 scripts/regression_analyzer.py data.csv --target sales --output result.json

详细用法

基本调用

python3 scripts/regression_analyzer.py <数据文件> --target <目标列> [选项]

指定回归类型

# 强制线性回归
python3 scripts/regression_analyzer.py data.csv -t y --type linear

# 强制逻辑回归
python3 scripts/regression_analyzer.py data.csv -t label --type logistic

# 自动检测（默认）
python3 scripts/regression_analyzer.py data.csv -t y --type auto

选择特征列

# 手动指定（逗号分隔）
python3 scripts/regression_analyzer.py data.csv -t price -f "sqft,bedrooms,bathrooms"

# 省略则自动使用所有数值列
python3 scripts/regression_analyzer.py data.csv -t price

参数说明

参数	缩写	必填	默认值	说明
`input`	—	是	—	输入文件路径（CSV/TSV/Excel/JSON）
`--target`	`-t`	是	—	目标变量（因变量）列名
`--features`	`-f`	否	全部数值列	自变量列名，逗号分隔
`--type`	`-T`	否	`auto`	回归类型：`linear` / `logistic` / `auto`
`--output`	`-o`	否	标准输出	结果 JSON 保存路径
`--no-const`	—	否	`false`	不添加截距项
`--keep-na`	—	否	`false`	保留缺失值行（调试用）

输出结构（JSON）

{
  "type": "linear",
  "r_squared": 0.8523,
  "r_squared_adj": 0.8471,
  "f_statistic": 162.34,
  "f_p_value": 0.0,
  "coefficients": {
    "sqft": {"coefficient": 135.42, "p_value": 0.0001, ...},
    "bedrooms": {"coefficient": 8021.5, "p_value": 0.032, ...}
  },
  "vif": {"sqft": 2.31, "bedrooms": 1.87},
  "interpretation": {
    "模型概述": ["R² = 0.8523（模型拟合优良…）"],
    "各变量解读": ["sqft：系数 = 135.42…正向影响…"]
  }
}

依赖

Python 3.8+
pandas
numpy
statsmodels
scipy

pip install pandas numpy statsmodels scipy