Ml Model Evaluation
Helps with machine learning, data modeling as part of agent workflows workflows.
Install on your platform
We auto-selected Claude Code based on this skill’s supported platforms.
Run in terminal (recommended)
claude mcp add ml-model-evaluation npx -- -y @trustedskills/ml-model-evaluation
Or manually add to ~/.claude/settings.json
{
"mcpServers": {
"ml-model-evaluation": {
"command": "npx",
"args": [
"-y",
"@trustedskills/ml-model-evaluation"
]
}
}
}Requires Claude Code (claude CLI). Run claude --version to verify your install.
About This Skill
What it does
This skill evaluates machine learning models based on provided metrics. It can analyze performance indicators like accuracy, precision, recall, and F1-score to provide insights into a model's effectiveness. The skill aims to help users understand how well their ML models are performing against defined criteria.
When to use it
- You have trained an ML model and need to quantify its performance.
- You want to compare the performance of different ML models on the same dataset.
- You're troubleshooting a poorly performing ML model and need to identify areas for improvement.
- You require automated reporting of ML model evaluation metrics.
Key capabilities
- Accepts various machine learning evaluation metrics as input.
- Provides analysis based on provided performance indicators.
- Offers insights into model effectiveness.
Example prompts
- "Evaluate this model's accuracy, precision, recall and F1-score: [metrics data]"
- "Analyze these results to determine if the model is performing adequately."
- “Compare the metrics for Model A and Model B.”
Tips & gotchas
The skill requires a structured input of evaluation metrics. Ensure the provided data is correctly formatted for accurate analysis.
Tags
TrustedSkills Verification
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Security Audits
| Gen Agent Trust Hub | Pass |
| Socket | Pass |
| Snyk | Pass |
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Passed automated security scans.