What types of ML models can the Model Evaluator assess?

It supports classification, regression, and ranking models, providing specific metrics like F1-score, RMSE, and NDCG respectively.

Can I compare multiple models at once?

Absolutely; it features a comparison tool that benchmarks accuracy, AUC, inference time, and model size across different candidates.

Does it support statistical significance testing?

Yes, it includes p-value calculations and confidence intervals to ensure model improvements are statistically meaningful.

How does it integrate with my development workflow?

It uses the SpecWeave framework to automatically generate evaluation reports and visualizations within your project's increment documentation.

Model Evaluator

Name: Model Evaluator
Author: anton-abyzov

byanton-abyzov

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データサイエンスとML

Conducts comprehensive machine learning model evaluations with advanced metrics, statistical validation, and automated reporting.

The Model Evaluator skill for SpecWeave provides an end-to-end framework for assessing ML models beyond simple accuracy. It automates the generation of detailed performance reports—including classification, regression, and ranking metrics—while performing statistical significance tests and cross-validation to ensure model reliability. Seamlessly integrated into the SpecWeave development workflow, it helps developers make data-driven deployment decisions by comparing multiple models and identifying potential issues like overfitting or class imbalance.

主な機能

01Multi-dimensional metrics for classification, regression, and ranking tasks

0213 GitHub stars

03Automated statistical validation including cross-validation and significance testing

04Visualized performance reports including confusion matrices and ROC curves

05Direct integration with SpecWeave increments for automated documentation

06Model comparison tools with inference time and size benchmarking

ユースケース

01Performing K-fold cross-validation to detect overfitting in training pipelines

02Validating a new model against a baseline before production deployment

03Comparing multiple architectures like XGBoost vs. Neural Nets for a specific dataset

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add anton-abyzov/specweave model-evaluator

For use in Claude.ai and ChatGPT

Download Skill