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Explainability Python Packages

Python packages with the GitHub topic explainability. Sorted by relevance, with stars and monthly downloads.
shap
shap

A game theoretic approach to explain the output of any machine learning model.

14.8M 25K 4K
interpretml
interpret-core

Fit interpretable models. Explain blackbox machine learning.

944K 7K 784
interpretml
interpret

Fit interpretable models. Explain blackbox machine learning.

429K 7K 784
wisent-ai
wisent

This is an open-source version of the representation engineering framework for stopping harmful outputs or hallucinations on the level of activations. 100% free, self-hosted and open-source.

77K 342 32
microsoft
raiutils

Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.

71K 2K 476
mmschlk
shapiq

Shapley Interactions and Shapley Values for Machine Learning

30K 733 60
microsoft
erroranalysis

Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.

26K 2K 476
microsoft
responsibleai

Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.

19K 2K 476
iancovert
sage-importance

For calculating global feature importance using Shapley values.

18K 289 33
keisen
tf-keras-vis

Neural network visualization toolkit for tf.keras

9K 337 47
salimamoukou
acv-dev

ACV is a python library that provides explanations for any machine learning model or data. It gives local rule-based explanations for any model or data and different Shapley Values for tree-based models.

8K 103 11
microsoft
raiwidgets

Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.

8K 2K 476
givasile
effector

Effector - a Python package for global and regional effect methods

8K 119 2
MAIF
shapash

🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models

8K 3K 385
kgd-al
abrain

ES-HyperNEAT Python implementation with C++ computations for NeuroEvolution, Reinforcement Learning and VfMRI

8K 6 0
hi-paris
xper

A methodology designed to measure the contribution of the features to the predictive performance of any econometric or machine learning model.

6K 18 1
chr5tphr
zennit

Zennit is a high-level framework in Python using PyTorch for explaining/exploring neural networks using attribution methods like LRP.

5K 243 35
snehankekre
streamlit-shap

streamlit-shap provides a wrapper to display SHAP plots in Streamlit.

5K 91 10
CAIIVS
raitap

Fully integrated pipeline to assess the transparency & robustness of AI models

5K 1 1
bramucas
xclingo

A tool for explainability and debugging in Answer Set Programming.

2K 15 5
salimamoukou
acv-exp

ACV is a python library that provides explanations for any machine learning model or data. It gives local rule-based explanations for any model or data and different Shapley Values for tree-based models.

2K 103 11
microsoft
rai-test-utils

Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.

2K 2K 476
csinva
imodelsx

Interpret text data with LLMs (sklearn compatible).

2K 175 27
microsoft
responsibleai-vision

SDK API to assess image Machine Learning models.

2K 2K 476
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