What metrics can this skill track?

The skill tracks a wide range of standard metrics, including accuracy, precision, recall, and F1-score, tailored to the specific model type.

Does this skill help with model selection?

Absolutely. It provides standardized data comparison across multiple models, making it easier to select the best-performing version for your specific use case.

How do I trigger the model evaluation process?

You can trigger the evaluation by asking Claude to 'evaluate model performance' or 'compare metrics', which invokes the /eval-model command.

Can I use this for time-series models?

Yes, since it integrates with the Nixtla-based plugins, it is highly effective for evaluating TimeGPT pipelines and other time-series forecasting models.

ML Model Evaluation Suite

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

byintent-solutions-io

Data Science & ML

Evaluates machine learning models by generating comprehensive performance metrics including accuracy, precision, and F1-score to guide model optimization.

About

The ML Model Evaluation Suite skill empowers Claude to perform deep-dive performance analysis of machine learning models. By integrating the model-evaluation-suite plugin, it allows users to trigger automated assessments using the /eval-model command, generating critical insights across various metrics like recall and precision. This skill is particularly valuable for developers and data scientists who need to compare multiple model versions, validate performance before deployment, or identify specific areas for algorithmic improvement within their AI workflows.

Key Features

Seamless integration with Nixtla-based AI and ML workflows
Comprehensive metric generation including accuracy, precision, and F1-score
0 GitHub stars
Automated performance benchmarking using the /eval-model command
Detailed reporting on model strengths and potential optimization areas
Comparative analysis between different machine learning models

Use Cases

Comparing the F1-scores of multiple classification models to select the best candidate
Identifying performance bottlenecks in predictive pipelines and model architectures
Validating model accuracy on held-out datasets before production deployment

About

Key Features

Seamless integration with Nixtla-based AI and ML workflows
Comprehensive metric generation including accuracy, precision, and F1-score
0 GitHub stars
Automated performance benchmarking using the /eval-model command
Detailed reporting on model strengths and potential optimization areas
Comparative analysis between different machine learning models

Use Cases

Comparing the F1-scores of multiple classification models to select the best candidate
Identifying performance bottlenecks in predictive pipelines and model architectures
Validating model accuracy on held-out datasets before production deployment