anchor
A model-agnostic method for generating high-precision rule-based explanations for black-box classifier predictions.
What is anchor?
Anchor is a Python library for generating high-precision, rule-based explanations for predictions made by black-box machine learning classifiers. It helps users understand why a model made a specific decision by identifying a minimal set of input features that 'anchor' the prediction, ensuring that changes to other features do not alter the outcome. This method is model-agnostic, meaning it works with any classifier that provides predictions, such as deep neural networks or ensemble models.
Data scientists, machine learning engineers, and researchers who need to interpret, debug, or audit black-box models, particularly in domains requiring transparency like healthcare, finance, or legal applications.
Developers choose Anchor for its ability to provide precise, interpretable local explanations without requiring access to the model's internals, its support for text and tabular data, and its foundation in peer-reviewed research, ensuring reliability and academic rigor.
Overview
Code for "High-Precision Model-Agnostic Explanations" paper
Use Cases
Best For
- Explaining individual predictions from black-box text classifiers
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