shibuiwilliam_mcp_server_scikit_learn
by shibuiwilliamScikit-learn Model Context Protocol Server
Overview
The Scikit-learn Model Context Protocol (MCP) Server provides a standardized interface for interacting with Scikit-learn models and datasets. This server simplifies the process of training, evaluating, and managing Scikit-learn models, making it easier to integrate machine learning workflows into your applications.
Features
- Train and evaluate Scikit-learn models: Easily train and evaluate models using Scikit-learn's robust algorithms.
- Handle datasets and data preprocessing: Streamline data preprocessing and dataset management.
- Model persistence and loading: Save and load models for future use.
- Feature engineering and selection: Perform advanced feature engineering and selection tasks.
- Model evaluation metrics: Access a variety of metrics to evaluate model performance.
- Cross-validation and hyperparameter tuning: Optimize models using cross-validation and hyperparameter tuning techniques.
Run This Project Locally
To run this project locally, follow these steps:
-
Clone the repository:
shell git clone https://github.com/yourusername/mcp-server-scikit-learn.git cd mcp-server-scikit-learn -
Launch the MCP inspector via npm:
shell npx @modelcontextprotocol/inspector uv --directory=src/mcp_server_scikit_learn run mcp-server-scikit-learn -
Access the URL displayed in your browser to begin debugging.
Alternatively, you can add this tool as an MCP server by including the following configuration in your settings:
{
"scikit-learn": {
"command": "uv",
"args": [
"--directory",
"/path/to/mcp-server-scikit-learn",
"run",
"mcp-server-scikit-learn"
]
}
}
Development
-
Create and activate a virtual environment:
shell python -m venv .venv source .venv/bin/activate # On Windows: .venv\Scripts\activate -
Install dependencies:
shell pip install -e ".[dev]" -
Run tests:
shell pytest -s -v tests/
License
This project is licensed under the MIT License. See the LICENSE file for more details.
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