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home / skills / jeremylongshore / claude-code-plugins-plus-skills / model-evaluation-metrics

model-evaluation-metrics skill

/skills/07-ml-training/model-evaluation-metrics

This skill helps you implement and validate model evaluation metrics with production-ready code, configurations, and best-practice guidance.

npx playbooks add skill jeremylongshore/claude-code-plugins-plus-skills --skill model-evaluation-metrics

Review the files below or copy the command above to add this skill to your agents.

Files (1)
SKILL.md
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---
name: "model-evaluation-metrics"
description: |
  Build model evaluation metrics operations. Auto-activating skill for ML Training.
  Triggers on: model evaluation metrics, model evaluation metrics
  Part of the ML Training skill category. Use when working with model evaluation metrics functionality. Trigger with phrases like "model evaluation metrics", "model metrics", "model".
allowed-tools: "Read, Write, Edit, Bash(python:*), Bash(pip:*)"
version: 1.0.0
license: MIT
author: "Jeremy Longshore <[email protected]>"
---

# Model Evaluation Metrics

## Overview

This skill provides automated assistance for model evaluation metrics tasks within the ML Training domain.

## When to Use

This skill activates automatically when you:
- Mention "model evaluation metrics" in your request
- Ask about model evaluation metrics patterns or best practices
- Need help with machine learning training skills covering data preparation, model training, hyperparameter tuning, and experiment tracking.

## Instructions

1. Provides step-by-step guidance for model evaluation metrics
2. Follows industry best practices and patterns
3. Generates production-ready code and configurations
4. Validates outputs against common standards

## Examples

**Example: Basic Usage**
Request: "Help me with model evaluation metrics"
Result: Provides step-by-step guidance and generates appropriate configurations


## Prerequisites

- Relevant development environment configured
- Access to necessary tools and services
- Basic understanding of ml training concepts


## Output

- Generated configurations and code
- Best practice recommendations
- Validation results


## Error Handling

| Error | Cause | Solution |
|-------|-------|----------|
| Configuration invalid | Missing required fields | Check documentation for required parameters |
| Tool not found | Dependency not installed | Install required tools per prerequisites |
| Permission denied | Insufficient access | Verify credentials and permissions |


## Resources

- Official documentation for related tools
- Best practices guides
- Community examples and tutorials

## Related Skills

Part of the **ML Training** skill category.
Tags: ml, training, pytorch, tensorflow, sklearn

Overview

This skill automates creation, validation, and guidance for model evaluation metrics within ML training workflows. It helps generate metric computations, reporting code, and configuration snippets that integrate with common frameworks. Use it to standardize evaluation practices across experiments and ensure reproducible, comparable results.

How this skill works

The skill inspects model outputs, labels, and experiment metadata to recommend and generate appropriate metric calculations (classification, regression, ranking, etc.). It produces ready-to-run code snippets, evaluation configurations, and validation checks that follow industry best practices. It can also suggest thresholds, aggregation strategies, and reporting formats for experiment tracking systems.

When to use it

  • You need standard metric implementations for classification, regression, or ranking models
  • Generating evaluation code for PyTorch, TensorFlow, or scikit-learn pipelines
  • Setting up experiment tracking and automated evaluation reports
  • Validating metric calculations for reproducibility and correctness
  • Tuning models and comparing performance across experiments

Best practices

  • Choose metrics aligned with business objectives (precision/recall for imbalance, MAE/RMSE for regression)
  • Compute group-level and aggregated metrics to detect distributional issues
  • Log raw predictions, targets, and metadata to enable reproducible recalculation
  • Set clear thresholds and confidence intervals, and document evaluation conditions
  • Automate validation checks to catch label leakage or metric miscalculation early

Example use cases

  • Generate classification evaluation pipeline with accuracy, precision, recall, F1, ROC-AUC and threshold analysis
  • Create regression evaluation script calculating MSE, RMSE, MAE, R², and error distributions
  • Produce evaluation config for experiment tracker (e.g., MLflow) that logs metrics, artifacts, and parameter snapshots
  • Validate a model comparison table across cross-validation folds with statistical significance checks
  • Automate post-training metric reports for CI/CD to gate model promotion

FAQ

Which metrics should I compute for imbalanced classification?

Focus on precision, recall, F1, and PR-AUC rather than accuracy. Consider class-wise metrics and calibration checks.

Can this skill generate code for my framework?

Yes. It produces framework-specific snippets for PyTorch, TensorFlow, and scikit-learn, plus generic Python utilities for metric calculation and reporting.