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
3k stars
38 watching
335 forks
Language: Jupyter Notebook
last commit: 24 days ago
Linked from 3 awesome lists
ethical-artificial-intelligenceexplainabilityexplainable-mlinterpretabilitylimemachine-learningpythonshaptransparency
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 |