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MLBugDetection
===========================================
Most machine learning explainability packages requires both trained models and the training data to create Explainer objects that explain the model's behavior. This package allows ceteris paribus analysis of features using only the trained model and one or more input samples.

Resources
===========================================
* Source code on `GitHub <https://github.com/jpgianfaldoni/MLBugDetection/>`_
* How to use? You can check the `Tutorial <https://jpgianfaldoni.github.io/MlbgTutorial/>`_
* Do you want to contribute? `Check here <https://github.com/jpgianfaldoni/MLBugDetection/blob/main/CONTRIBUTING.md>`_

Topics
===========================================
Here, you can see how to install the library and see all modules and functions.

.. toctree::
   :maxdepth: 2
   
   installation
   analysis
   monotonic
   critical
   calibration
   sanity
   data
   model

Indices and tables
===========================================

* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`