How do I trigger a model evaluation in Claude?

You can trigger it by using natural language phrases like 'evaluate model', 'check model performance', or 'compare validation results' within Claude Code.

What metrics can this skill evaluate?

It evaluates key machine learning metrics including accuracy, precision, recall, F1-score, and other relevant performance indicators using the model-evaluation-suite plugin.

Do I need to install a specific plugin to use this skill?

Yes, this skill is built to integrate with the model-evaluation-suite plugin to execute the underlying evaluation logic.

Is this skill suitable for validating models before deployment?

Absolutely. It is specifically designed to identify areas for improvement and validate performance metrics to ensure a model is production-ready.

Can I compare two different models with this skill?

Yes, the skill is designed to run evaluations on multiple models and provide side-by-side comparisons of their performance results.

ML Model Evaluation Suite

Name: ML Model Evaluation Suite
Author: jeremylongshore

byjeremylongshore

•

883

数据科学与机器学习

Evaluates machine learning models by calculating performance metrics like accuracy, precision, and F1-score to guide model optimization.

关于

The ML Model Evaluation Suite skill equips Claude with the ability to perform rigorous, data-driven evaluations of machine learning models. By leveraging a comprehensive suite of metrics, it allows developers to analyze model performance across key indicators such as recall, precision, and accuracy, facilitating comparative analysis between multiple models and identifying specific areas for improvement before production deployment.

主要功能

Automated calculation of standard metrics like Accuracy, F1-score, and Recall
883 GitHub stars
Performance validation using held-out datasets for unbiased results
Comparative analysis capabilities for benchmarking multiple model versions
Actionable insights into model strengths and potential weaknesses
Seamless integration with the /eval-model command for rapid testing

使用场景

Assessing the accuracy of classification or regression models during development
Validating model performance metrics before final deployment to production
Comparing the F1-scores of different model architectures to select the best candidate

关于

主要功能

Automated calculation of standard metrics like Accuracy, F1-score, and Recall
883 GitHub stars
Performance validation using held-out datasets for unbiased results
Comparative analysis capabilities for benchmarking multiple model versions
Actionable insights into model strengths and potential weaknesses
Seamless integration with the /eval-model command for rapid testing

使用场景

Assessing the accuracy of classification or regression models during development
Validating model performance metrics before final deployment to production
Comparing the F1-scores of different model architectures to select the best candidate