How does Claude evaluate the models?

It uses the /eval-model command to trigger the evaluation suite plugin, which analyzes the model against provided datasets and generates a performance report.

Which metrics are supported by this skill?

The skill supports a wide range of standard metrics including accuracy, precision, recall, and F1-score, as well as specialized benchmarks for time-series models.

Can I compare multiple models at once?

Yes, you can request a comparative analysis to see how different models or versions perform against the same dataset to facilitate informed decision-making.

Does this work with Nixtla TimeGPT?

Yes, it is specifically designed to work with the Nixtla plugin ecosystem, making it ideal for evaluating TimeGPT pipelines and time-series models.

ML Model Evaluation Suite

Name: ML Model Evaluation Suite
Author: intent-solutions-io

byintent-solutions-io

データサイエンスとML

Evaluates machine learning model performance using standardized metrics like accuracy, precision, and F1-score to guide model optimization and validation.

概要

This skill empowers Claude to conduct thorough performance assessments of machine learning models within the development environment. By leveraging the model-evaluation-suite plugin, it automates the calculation of critical validation metrics, benchmarks different model versions, and provides actionable insights for improving model reliability before deployment. It is particularly useful for data scientists and ML engineers who need to validate TimeGPT pipelines or custom AI models directly through natural language commands in Claude Code.

主な機能

Automated metric calculation including Accuracy, Precision, Recall, and F1-score
0 GitHub stars
Detailed performance reporting via natural language queries
Validation of models on held-out datasets for deployment readiness
Comparative analysis between multiple models or model versions
Seamless integration with Nixtla and TimeGPT pipelines

ユースケース

Validating a time-series forecasting model's accuracy before production release
Identifying specific areas of model failure through detailed metric breakdowns
Comparing the F1-scores of different classification models to select the best candidate

概要

主な機能

Automated metric calculation including Accuracy, Precision, Recall, and F1-score
0 GitHub stars
Detailed performance reporting via natural language queries
Validation of models on held-out datasets for deployment readiness
Comparative analysis between multiple models or model versions
Seamless integration with Nixtla and TimeGPT pipelines

ユースケース

Validating a time-series forecasting model's accuracy before production release
Identifying specific areas of model failure through detailed metric breakdowns
Comparing the F1-scores of different classification models to select the best candidate