What metrics can this Claude Code skill calculate?

The skill calculates a comprehensive suite of metrics including accuracy, precision, recall, F1-score, and other relevant performance indicators for ML models.

How do I trigger the model evaluation process?

You can trigger the skill by asking Claude to 'evaluate model', 'check model performance', 'run testing metrics', or 'validate results'.

Is this suitable for pre-deployment validation?

Yes, it is specifically designed to help validate model performance on held-out datasets to ensure reliability before moving to production.

Can I compare two different models at once?

Yes, you can ask Claude to compare the performance of multiple models (e.g., 'Compare Model A and Model B') to see side-by-side metric comparisons.

Does this work with any ML framework?

This skill is designed to work within the Nixtla plugin ecosystem and integrates specifically with the model-evaluation-suite plugin for standardized reporting.

Machine Learning Model Evaluator

Name: Machine Learning Model Evaluator
Author: intent-solutions-io

byintent-solutions-io

데이터 과학 및 ML

Evaluates machine learning model performance using a comprehensive suite of metrics to ensure accuracy and reliability.

소개

This skill empowers Claude to perform deep diagnostic evaluations of machine learning models directly within your development environment. By leveraging the model-evaluation-suite plugin, it automates the calculation of critical performance metrics like F1-score, precision, and recall, enabling developers to compare model versions, validate performance on held-out datasets, and identify specific areas for optimization before deployment.

주요 기능

Seamless integration with the /eval-model command for automated testing
Context-aware analysis of model validation and testing requests
0 GitHub stars
Multi-model comparison capabilities for benchmarking different architectures
Comprehensive performance metric generation including Accuracy, Precision, and Recall
Actionable insights for model selection and optimization workflows

사용 사례

Validating model accuracy on new datasets before final deployment
Conducting detailed diagnostic checks to identify model regressions or performance gaps
Benchmarking multiple ML models to select the best performer for production

소개

주요 기능

Seamless integration with the /eval-model command for automated testing
Context-aware analysis of model validation and testing requests
0 GitHub stars
Multi-model comparison capabilities for benchmarking different architectures
Comprehensive performance metric generation including Accuracy, Precision, and Recall
Actionable insights for model selection and optimization workflows

사용 사례

Validating model accuracy on new datasets before final deployment
Conducting detailed diagnostic checks to identify model regressions or performance gaps
Benchmarking multiple ML models to select the best performer for production