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shap
shap

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

14.5M 25K 4K
interpretml
interpret-core

Fit interpretable models. Explain blackbox machine learning.

957K 7K 783
tensorflow
tensorflow-decision-forests

A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models in Keras.

645K 693 116
google
ydf

A library to train, evaluate, interpret, and productionize decision forest models such as Random Forest and Gradient Boosted Decision Trees.

535K 652 78
pytorch
captum

Model interpretability and understanding for PyTorch

482K 6K 557
interpretml
interpret

Fit interpretable models. Explain blackbox machine learning.

387K 7K 783
ottenbreit-data-science
aplr

APLR builds predictive, interpretable regression and classification models using Automatic Piecewise Linear Regression. It often rivals tree-based methods in predictive accuracy while offering smoother and interpretable predictions.

215K 23 5
jacobgil
grad-cam

Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.

80K 13K 2K
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.

72K 2K 476
csinva
imodels

Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).

52K 2K 138
ndif-team
nnsight

The nnsight package enables interpreting and manipulating the internals of deep learned models.

42K 917 87
ModelOriented
dalex

moDel Agnostic Language for Exploration and eXplanation

36K 1K 170
linkedin
fasttreeshap

Fast SHAP value computation for interpreting tree-based models

30K 555 38
SeldonIO
alibi

Algorithms for explaining machine learning models

30K 3K 264
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.

27K 2K 476
mmschlk
shapiq

Shapley Interactions and Shapley Values for Machine Learning

26K 722 58
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.

22K 2K 476
frgfm
torchcam

Class activation maps for your PyTorch models (CAM, Grad-CAM, Grad-CAM++, Smooth Grad-CAM++, Score-CAM, SS-CAM, IS-CAM, XGrad-CAM, Layer-CAM)

15K 2K 224
iancovert
sage-importance

For calculating global feature importance using Shapley values.

13K 287 33
yohanpoul
etzchaim

A diagnosable brain for your LLM. Cognitive architecture in the SOAR/ACT-R/CLARION/LIDA lineage, for the LLM era. Apache 2.0.

10K 1 0
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.

9K 2K 476
MAIF
shapash

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

9K 3K 383
a9lim
saklas

Activation steering and trait monitoring for HuggingFace transformers

8K 3 0
BCG-X-Official
gamma-facet

Human-explainable AI.

7K 532 46
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