Metadata-Version: 2.1
Name: gamma-facet
Version: 1.2.2
Summary: Human-explainable AI.
Home-page: https://github.com/BCG-Gamma/facet
License: Apache Software License v2.0
Author: Boston Consulting Group (BCG)
Requires-Python: >=3.6,<4a
Description-Content-Type: text/x-rst
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: MacOS
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: Unix
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Topic :: Scientific/Engineering
Requires-Dist: gamma-pytools  ~=1.2,>=1.2.1
Requires-Dist: matplotlib     ~=3.0
Requires-Dist: numpy          >=1.17,<2a
Requires-Dist: packaging      >=20
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Project-URL: Documentation, https://bcg-gamma.github.io/facet/
Project-URL: Repository, https://github.com/BCG-Gamma/facet
Provides-Extra: docs
Provides-Extra: testing

FACET is an open source library for human-explainable AI.
It combines sophisticated model inspection and model-based simulation to enable better
explanations of your supervised machine learning models.

FACET is composed of the following key components:


**Model Inspection**

FACET introduces a new algorithm to quantify dependencies and
interactions between features in ML models.
This new tool for human-explainable AI adds a new, global
perspective to the observation-level explanations provided by the
popular `SHAP <https://shap.readthedocs.io/en/stable/>`__ approach.
To learn more about FACET’s model inspection capabilities, see the
getting started example below.


**Model Simulation**

FACET’s model simulation algorithms use ML models for
*virtual experiments* to help identify scenarios that optimise
predicted outcomes.
To quantify the uncertainty in simulations, FACET utilises a range
of bootstrapping algorithms including stationary and stratified
bootstraps.
For an example of FACET’s bootstrap simulations, see the
quickstart example below.


**Enhanced Machine Learning Workflow**

FACET offers an efficient and transparent machine learning
workflow, enhancing
`scikit-learn <https://scikit-learn.org/stable/index.html>`__'s
tried and tested pipelining paradigm with new capabilities for model
selection, inspection, and simulation.
FACET also introduces
`sklearndf <https://github.com/BCG-Gamma/sklearndf>`__, an augmented
version of *scikit-learn* with enhanced support for *pandas* data
frames that ensures end-to-end traceability of features.


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License
---------------------------

FACET is licensed under Apache 2.0 as described in the
`LICENSE <https://github.com/BCG-Gamma/facet/blob/develop/LICENSE>`_ file.

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