What metrics can this skill evaluate?

The skill provides a comprehensive suite of metrics including accuracy, precision, recall, F1-score, and other standard performance indicators relevant to machine learning models.

Does this skill work with Nixtla's TimeGPT pipelines?

Yes, as part of the plugins-nixtla repository, it is built to integrate with Nixtla-based workflows, including benchmarking time-series and forecasting models.

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

Yes, this skill is designed to analyze and present comparative metrics for multiple models, helping you identify which version performs better on your specific dataset.

How do I trigger the model evaluation process?

You can trigger the skill by asking Claude to 'evaluate model', 'test metrics', or 'compare performance'. Claude will then use the /eval-model command to process the request.

Machine Learning Model Evaluation

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

byintent-solutions-io

Ciencia de Datos y ML

Evaluates machine learning model performance using a comprehensive suite of metrics to facilitate data-driven optimization and validation.

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The Machine Learning Model Evaluation skill empowers Claude to perform in-depth analysis of ML models through the model-evaluation-suite plugin. It automates the calculation of critical performance indicators such as accuracy, precision, and F1-score, allowing developers to benchmark different models, identify specific areas for improvement, and validate performance on held-out datasets. By integrating directly into the Claude Code environment, it streamlines the model development lifecycle from initial testing to production readiness within the Nixtla ecosystem.

Características Principales

Side-by-side comparison of multiple model performances
Seamless integration with Nixtla model evaluation pipelines
Detailed performance insight reporting via /eval-model
Validation of model results on representative datasets
Automated metric generation for accuracy, precision, and recall
0 GitHub stars

Casos de Uso

Validating model performance benchmarks before production deployment
Comparing F1-scores between two competing natural language processing models
Assessing the classification accuracy of an image recognition model

Acerca de

Características Principales

Side-by-side comparison of multiple model performances
Seamless integration with Nixtla model evaluation pipelines
Detailed performance insight reporting via /eval-model
Validation of model results on representative datasets
Automated metric generation for accuracy, precision, and recall
0 GitHub stars

Casos de Uso

Validating model performance benchmarks before production deployment
Comparing F1-scores between two competing natural language processing models
Assessing the classification accuracy of an image recognition model