Linear Regression MCP Server

Name: Linear Regression MCP Server
Author: HeetVekariya

@HeetVekariya

代码仓库

基本信息

分类

版本控制

传输方式

stdio

作者

HeetVekariya

关于 Linear Regression MCP Server

MCP server for training Linear Regression Model.

#mcp#linear-regression#machine-learning

Linear Regression MCP Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "Linear-Regression-MCP": {
      "command": "uv",
      "args": [
        "sync"
      ]
    }
  }
}

Linear Regression MCP Server 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is Linear Regression MCP?

Linear Regression MCP is a Model Context Protocol server that lets Claude train a linear regression model end-to-end by simply uploading a CSV file. It automates data preprocessing, training, and evaluation (RMSE calculation), making it ideal for users who want to run ML workflows through natural language.

How to use Linear Regression MCP?

Clone the repository, install uv, run uv sync, then configure Claude Desktop by adding the server path to claude_desktop_config.json. Once linked, Claude can invoke tools like upload_file, get_columns_info, and train_linear_regression_model.

Key features of Linear Regression MCP

End-to-end ML model training lifecycle with Claude.
Upload any CSV dataset for automatic processing.
Automatic categorical column label encoding.
RMSE-based evaluation after training.
Tools to inspect column info and data types.
Open-source and welcomes contributions.

Use cases of Linear Regression MCP

Train a linear regression model on a local CSV without writing code.
Rapidly prototype regression models using natural language instructions.
Automate data preprocessing and model evaluation for educational or small-scale projects.

FAQ from Linear Regression MCP

—

版本控制分类下的更多 MCP 服务器

GitLab

modelcontextprotocol

Model Context Protocol Servers

88.1K

MCP Linear

tacticlaunch

MCP server that enables AI assistants to interact with Linear project management system through natural language, allowing users to retrieve, create, and update issues, projects, and teams.

141