PyPI · npm
Arize-ai/phoenix
AI Observability & Evaluation
Licencia propia28 ago 2026 · métricas 2.10.0
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#explainability
Todos los repositorios del registro público que llevan esta etiqueta, procedente de sus topics de GitHub o de las palabras clave que publican sus registros de paquetes. La salud se mide con la misma metodología versionada que el resto del registro.
9 registros
PyPI · npm
shap/shap
A game theoretic approach to explain the output of any machine learning model.
MIT16 sept 2026 · métricas 2.10.0
PyPI
mmschlk/shapiq
Shapley Interactions and Shapley Values for Machine Learning
MIT14 sept 2026 · métricas 2.10.0
PyPI · npm
interpretml/interpret
Fit interpretable models. Explain blackbox machine learning.
MIT17 sept 2026 · métricas 2.10.0
PyPI
CodeBoarding/CodeBoarding
Interactive architecture diagrams for codebases
MIT20 sept 2026 · métricas 2.10.0
PyPI
GiacomoSaccaggi/scomp_link
End-to-end ML toolkit automating the complete workflow — data profiling, preprocessing, feature engineering, model selection, training, validation, explainability, drift monitoring, fairness checks, and interactive HTML reporting. Includes a full CLI for zero-code ML pipelines. Python 3.10+.
MIT9 sept 2026 · métricas 2.10.0
PyPI
Arize-ai/client_python
A Python client to interact with Arize API
Apache-2.014 sept 2026 · métricas 2.10.0
PyPI
givasile/effector
Effector - a Python package for global and regional effect methods
MIT10 sept 2026 · métricas 2.10.0
PyPI
solvi-ai/solvi
A runtime where decision models propose and your code decides: typed questions, hard rules, quotes checked against the text, escalation with a guaranteed error budget, replayable traces. Works with any System One model.
Apache-2.011 oct 2026 · métricas 2.10.0