Explainer Principles

PyXAI includes a module Explainer which provides different methods for explaining decisions made by ML models.

The Concepts page explains:

How to initialize this module (Explainer.initialize and <Explainer Object>.set_instance)
The concepts on which it is based (mainly the binary representation of an instance)
How to display an explanation (<Explainer Object>.to_features)

The other pages within this section present several miscellaneous features:

Theories are representation of pieces of knowledge about the dataset. PyXAI offers the possibility of encoding a theory calculating explanations in order to avoid the computation of impossible explanations.
The Explainer offers the possibility to process user preferences (prefer some explanations to others and exclude some features): Preferences
How to use a time limit when calculating explanations ? Time Limit

Several kinds of explanations can be computed according to the properties they hold (direct, sufficient, majoritary, tree-specific, contrastive):

How to compute explanations for classification tasks ? Explaining Classification
How to compute explanations for regression tasks ? Explaining Regression

To finish the Visualization Of Explanations page present the PyXAI’s Graphical User Interface (GUI).

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