What metrics can this skill calculate?

It generates a comprehensive suite of metrics including accuracy, precision, recall, and F1-score to provide a multi-dimensional view of performance.

Is this skill suitable for production validation?

Absolutely. It is designed to help validate model performance against held-out datasets to ensure reliability and accuracy before live deployment.

How do I trigger the model evaluation?

Simply ask Claude to 'evaluate my model' or 'check performance', and it will use the /eval-model command to begin the analysis.

Can I compare two different models side-by-side?

Yes, you can request a comparison between models, and the skill will evaluate both to present a detailed comparison of their performance indicators.

Machine Learning Model Evaluator

Name: Machine Learning Model Evaluator
Author: jeremylongshore

byjeremylongshore

•

883

Data Science & ML

Evaluates machine learning model performance using a comprehensive suite of metrics like accuracy, precision, and F1-score.

About

This skill streamlines the machine learning development lifecycle by allowing Claude to perform rigorous performance analysis on models directly within your terminal. By leveraging the model-evaluation-suite plugin, it automates the calculation of critical metrics, facilitates model comparison, and identifies optimization opportunities, helping developers validate models before deployment to ensure high-quality production outcomes.

Key Features

Direct integration with the /eval-model command for instant results
In-depth insights for model selection and hyperparameter optimization
Analysis of model performance on specific held-out datasets
883 GitHub stars
Comparative analysis between multiple model architectures
Automated performance metric generation including Accuracy and F1-score

Use Cases

Identifying specific areas for model refinement based on precision and recall scores
Comparing different iterations or versions of a machine learning model
Validating model performance metrics before production deployment

About

Key Features

Direct integration with the /eval-model command for instant results
In-depth insights for model selection and hyperparameter optimization
Analysis of model performance on specific held-out datasets
883 GitHub stars
Comparative analysis between multiple model architectures
Automated performance metric generation including Accuracy and F1-score

Use Cases

Identifying specific areas for model refinement based on precision and recall scores
Comparing different iterations or versions of a machine learning model
Validating model performance metrics before production deployment