Mcp Server Scikit Learn
What is Mcp Server Scikit Learn
mcp-server-scikit-learn is a Model Context Protocol server designed for Scikit-learn, providing a standardized interface for interacting with Scikit-learn models and datasets.
Use cases
Use cases include building predictive models, conducting experiments with different algorithms, performing data preprocessing tasks, and optimizing model performance through hyperparameter tuning.
How to use
To use mcp-server-scikit-learn, clone the repository from GitHub, navigate to the project directory, and run the MCP inspector or add it as an MCP server using the provided JSON configuration.
Key features
Key features include training and evaluating Scikit-learn models, handling datasets and data preprocessing, model persistence and loading, feature engineering and selection, model evaluation metrics, and cross-validation with hyperparameter tuning.
Where to use
mcp-server-scikit-learn can be used in various fields such as data science, machine learning, and artificial intelligence, particularly in projects that require model training and evaluation.
Clients Supporting MCP
The following are the main client software that supports the Model Context Protocol. Click the link to visit the official website for more information.
Overview
What is Mcp Server Scikit Learn
mcp-server-scikit-learn is a Model Context Protocol server designed for Scikit-learn, providing a standardized interface for interacting with Scikit-learn models and datasets.
Use cases
Use cases include building predictive models, conducting experiments with different algorithms, performing data preprocessing tasks, and optimizing model performance through hyperparameter tuning.
How to use
To use mcp-server-scikit-learn, clone the repository from GitHub, navigate to the project directory, and run the MCP inspector or add it as an MCP server using the provided JSON configuration.
Key features
Key features include training and evaluating Scikit-learn models, handling datasets and data preprocessing, model persistence and loading, feature engineering and selection, model evaluation metrics, and cross-validation with hyperparameter tuning.
Where to use
mcp-server-scikit-learn can be used in various fields such as data science, machine learning, and artificial intelligence, particularly in projects that require model training and evaluation.
Clients Supporting MCP
The following are the main client software that supports the Model Context Protocol. Click the link to visit the official website for more information.
Content
mcp-server-scikit-learn: MCP server for Scikit-learn
Overview
This is a Model Context Protocol server for Scikit-learn, providing a standardized interface for interacting with Scikit-learn models and datasets.
Features
- Train and evaluate Scikit-learn models
- Handle datasets and data preprocessing
- Model persistence and loading
- Feature engineering and selection
- Model evaluation metrics
- Cross-validation and hyperparameter tuning
Run this project locally
This project is not yet set up for ephemeral environments (e.g. uvx usage). Run this project locally by cloning this repo:
git clone https://github.com/yourusername/mcp-server-scikit-learn.git
cd mcp-server-scikit-learn
You can launch the MCP inspector via npm:
npx @modelcontextprotocol/inspector uv --directory=src/mcp_server_scikit_learn run mcp-server-scikit-learn
Upon launching, the Inspector will display a URL that you can access in your browser to begin debugging.
OR Add this tool as a MCP server:
{
"scikit-learn": {
"command": "uv",
"args": [
"--directory",
"/path/to/mcp-server-scikit-learn",
"run",
"mcp-server-scikit-learn"
]
}
}Development
- Create and activate a virtual environment:
python -m venv .venv
source .venv/bin/activate # On Windows: .venv\Scripts\activate
- Install dependencies:
pip install -e ".[dev]"
- Run tests:
pytest -s -v tests/
License
Dev Tools Supporting MCP
The following are the main code editors that support the Model Context Protocol. Click the link to visit the official website for more information.
