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Human-explainable AI.
| Date | Stars |
|---|---|
| 2026-07-25 | 533 |
| 2026-07-28 | 533 |
| 2026-07-30 | 533 |
| 2026-08-06 | 533 |
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growth rate 0.00%/day
.. image:: sphinx/source/_images/Gamma_Facet_Logo_RGB_LB.svg
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:
+-----------------+-----------------------------------------------------------------------+
| |spacer| | **Model Inspection** |
| | |
| |inspect| | 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. |
+-----------------+-----------------------------------------------------------------------+
| |spacer| | **Model Simulation** |
| | |
| |sim| | 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. |
+-----------------+-----------------------------------------------------------------------+
| |spacer| | **Enhanced Machine Learning Workflow** |
| | |
| |pipe| | 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-X-Official/sklearndf>`__ |
| | [`documentation <https://bcg-x-official.github.io/sklearndf/index.html>`__]|
| | an augmented version of *scikit-learn* with enhanced support for |
| | *pandas* data frames that ensures end-to-end traceability of features.|
+-----------------+-----------------------------------------------------------------------+
.. Begin-Badges
|pypi| |conda| |azure_build| |azure_code_cov|
|python_versions| |code_style| |made_with_sphinx_doc| |License_badge|
.. End-Badges
Installation
------------
FACET supports both PyPI and Anaconda.
We recommend to install FACET into a dedicated environment.
Anaconda
~~~~~~~~
.. code-block:: sh
conda create -n facet
conda activate facet
conda install -c bcg_gamma -c conda-forge gamma-facet
Pip
~~~
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:409665abcfb2da7c, topic:interpretability, topic:explainable-ai, desc:explainable