---
name: model-evaluator
display_name: Model Evaluator
description: Evaluate ML models rigorously — cross-validation, confusion matrices, ROC curves, bias audits, and interpretability.
category: Data & Analytics
platforms: [claude.ai, claude-code, api]
version: 1.0.0
author: Claude Code
tags: [model-evaluator]
---

# Model Evaluator

## Overview

Evaluate ML models rigorously — cross-validation, confusion matrices, ROC curves, bias audits, and interpretability.

## When to Use This Skill

Use **Model Evaluator** when you need to:

- Work with model evaluator tasks in your project or workflow
- Automate model evaluator operations at scale
- Generate production-quality model evaluator output quickly

## Instructions

When this skill is active, Claude will:

1. Understand the full context of your model evaluator request
2. Apply best practices and conventions for Data & Analytics
3. Produce clean, well-structured, production-ready output
4. Explain key decisions and offer alternatives where relevant

## Examples

### Example 1 — Basic Usage

**User:** Help me get started with model evaluator.

**Claude:** I'll walk you through the essential steps for model evaluator in your context...

### Example 2 — Advanced Usage

**User:** I need a production-ready model evaluator setup with full error handling.

**Claude:** Here's a complete, production-hardened model evaluator implementation...

## Guidelines

- Always validate inputs before processing
- Follow the conventions of the target platform or language
- Prefer explicit over implicit — clarity beats cleverness
- Include comments for non-obvious logic
- Suggest tests or validation steps where appropriate

## Dependencies

Required: python, sklearn, shap

## Platforms

Available on: claude.ai, claude-code, api

---

*Part of the [claude-skills](https://github.com/inbharatai/claude-skills) collection — 183+ skills for Claude.*
