shapash

Model explainer

Provides visualizations and explanations to help understand machine learning model interactions and decisions

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

GitHub

3k stars
38 watching
335 forks
Language: Jupyter Notebook
last commit: 24 days ago
Linked from 3 awesome lists

ethical-artificial-intelligenceexplainabilityexplainable-mlinterpretabilitylimemachine-learningpythonshaptransparency

Backlinks from these awesome lists:

Related projects:

Repository Description Stars
shap/shap Provides an algorithm to explain the output of machine learning models using game theory and Shapley values. 22,876
jphall663/interpretable_machine_learning_with_python Teaching software developers how to build transparent and explainable machine learning models using Python 673
bgreenwell/fastshap Provides an efficient approach to computing Shapley values for explaining machine learning model predictions. 116
trekhleb/homemade-machine-learning Practices implementing popular machine learning algorithms from scratch to gain a deeper understanding of their mathematics 23,121
h2oai/mli-resources Provides tools and techniques for interpreting machine learning models 484
interpretml/interpret An open-source package for explaining machine learning models and promoting transparency in AI decision-making 6,296
dfki-interactive-machine-learning/arasif Provides sentence embeddings for Arabic languages using pre-trained word embeddings and Smooth Inverse Frequency algorithm 5
interpretml/dice Provides counterfactual explanations for machine learning models to facilitate interpretability and understanding. 1,364
csinva/imodels An open-source package that provides interpretable machine learning models compatible with scikit-learn. 1,399
christophm/interpretable-ml-book A comprehensive resource for explaining the decisions and behavior of machine learning models. 4,794
mostafa-samir/how-machine-learning-works An implementation of Manning Publications' How Machine Learning Works book in Python using Jupyter Notebook 4
h2oai/article-information-2019 A framework for building and evaluating machine learning systems with high accuracy and interpretability, particularly in human-centered applications. 13
ethicalml/xai An eXplainability toolbox for machine learning that enables data analysis and model evaluation to mitigate biases and improve performance 1,125
trusted-ai/aix360 A toolkit for explaining complex AI models and data-driven insights 1,633
giskard-ai/giskard Automates detection and evaluation of performance, bias, and security issues in AI applications 4,071