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Author:
Vaibhav Kulshrestha
Summary:
A comprehensive Python package for interpreting and explaining machine learning and deep learning models. Includes support for feature attention mechanisms and integrates popular explanation methods such as LIME, SHAP, Grad-CAM, permutation importance, and saliency maps. Offers a unified interface for tabular, image, and other data types to enhance model transparency and interpretability.
Latest version:
0.5.0
Required dependencies:
dice-ml
|
fairlearn
|
joblib
|
lime
|
matplotlib
|
numpy
|
pandas
|
plotly
|
scikit-learn
|
seaborn
|
shap
|
streamlit
|
torch
|
xgboost
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