Author: Janis Klaise

Seldon releases Alibi Explain 0.5.0

The Seldon data science team are delighted to announce the release of v0.5.0 of Alibi Explain with three new techniques for explaining the predictions of machine learning models. This release features Integrated Gradients for Tensorflow models, Accumulated Local Effects for black-box models and TreeSHAP explanations for tree-based models such as gradient boosted models and random…

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Seldon adds SHAP to the Alibi explainable AI library

One of the biggest blockers to leveraging neural networks across industries is that modern machine learning models are an algorithmic “black box”. Explainable AI is a relatively new area of research which allows organisations to confidently deploy models into production by providing a human-interpretable explanation for model outputs. There were various methodologies and research papers…

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