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68 lines
1.9 KiB
Markdown
68 lines
1.9 KiB
Markdown
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---
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name: model-evaluator
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display_name: Model Evaluator
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description: Evaluate ML models rigorously — cross-validation, confusion matrices, ROC curves, bias audits, and interpretability.
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category: Data & Analytics
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platforms: [claude.ai, claude-code, api]
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version: 1.0.0
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author: Claude Code
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tags: [model-evaluator]
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---
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# Model Evaluator
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## Overview
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Evaluate ML models rigorously — cross-validation, confusion matrices, ROC curves, bias audits, and interpretability.
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## When to Use This Skill
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Use **Model Evaluator** when you need to:
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- Work with model evaluator tasks in your project or workflow
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- Automate model evaluator operations at scale
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- Generate production-quality model evaluator output quickly
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## Instructions
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When this skill is active, Claude will:
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1. Understand the full context of your model evaluator request
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2. Apply best practices and conventions for Data & Analytics
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3. Produce clean, well-structured, production-ready output
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4. Explain key decisions and offer alternatives where relevant
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## Examples
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### Example 1 — Basic Usage
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**User:** Help me get started with model evaluator.
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**Claude:** I'll walk you through the essential steps for model evaluator in your context...
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### Example 2 — Advanced Usage
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**User:** I need a production-ready model evaluator setup with full error handling.
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**Claude:** Here's a complete, production-hardened model evaluator implementation...
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## Guidelines
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- Always validate inputs before processing
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- Follow the conventions of the target platform or language
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- Prefer explicit over implicit — clarity beats cleverness
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- Include comments for non-obvious logic
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- Suggest tests or validation steps where appropriate
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## Dependencies
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Required: python, sklearn, shap
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## Platforms
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Available on: claude.ai, claude-code, api
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---
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*Part of the [claude-skills](https://github.com/inbharatai/claude-skills) collection — 183+ skills for Claude.*
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