seldonModel interpretability

Alibi is designed to help explain the predictions of machine learning models and gauge the confidence of those predictions.

The library is designed to support the widest possible range of models that use black-box methods.The open-source project goal is to increase the capabilities for inspecting the performance of models with respect to concept drift and algorithmic bias.


* Provide high quality reference implementations of black-box ML model explanation algorithms

* Define a consistent API for interpretable ML methods
* Support multiple use cases (e.g. tabular, text and image data classification, regression)
* Implement the latest model explanation, concept drift, algorithmic bias detection and other ML model monitoring and interpretation methods.

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Tutorial and documentation

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