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:
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 approach. To learn more about FACET’s model inspection capabilities, see the getting started example below.
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’s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces sklearndf, an augmented version of scikit-learn with enhanced support for pandas data frames that ensures end-to-end traceability of features.
FACET is licensed under Apache 2.0 as described in the LICENSE file.
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