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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Data Science & ML

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

About

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.

Key Features

Multi-dimensional metrics for classification, regression, and ranking tasks
13 GitHub stars
Automated statistical validation including cross-validation and significance testing
Visualized performance reports including confusion matrices and ROC curves
Direct integration with SpecWeave increments for automated documentation
Model comparison tools with inference time and size benchmarking

Use Cases

Performing K-fold cross-validation to detect overfitting in training pipelines
Validating a new model against a baseline before production deployment
Comparing multiple architectures like XGBoost vs. Neural Nets for a specific dataset

About

Key Features

Multi-dimensional metrics for classification, regression, and ranking tasks
13 GitHub stars
Automated statistical validation including cross-validation and significance testing
Visualized performance reports including confusion matrices and ROC curves
Direct integration with SpecWeave increments for automated documentation
Model comparison tools with inference time and size benchmarking

Use Cases

Performing K-fold cross-validation to detect overfitting in training pipelines
Validating a new model against a baseline before production deployment
Comparing multiple architectures like XGBoost vs. Neural Nets for a specific dataset