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Author:
Javier Pérez Vargas
Summary:
This extension aims to expand InterpretML by integrating probabilistic models while leveraging the existing explanation mechanisms provided by the library. By doing so, we enable users to analyze uncertainty, quantify probabilistic predictions, and gain deeper insights into model behavior beyond point estimates.
Latest version:
0.1.6
Required dependencies:
dash
|
dash-cytoscape
|
flask
|
gevent
|
ipython
|
joblib
|
loguru
|
matplotlib-inline
|
numpy
|
pandas
|
pyagrum
|
requests
|
scikit-learn
|
scipy
|
tensorboard
|
torch
|
tqdm
|
werkzeug
Optional dependencies:
aplr
|
dill
|
ipykernel
|
ipython
|
ipywidgets
|
jupyter
|
lime
|
nbconvert
|
plotly
|
psutil
|
pytest
|
pytest-cov
|
pytest-runner
|
pytest-xdist
|
ruff
|
salib
|
scikit-learn
|
selenium
|
shap
|
skope-rules
|
treeinterpreter
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