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Researchers from the University of California San Diego have developed a mathematical formula that explains how neural networks learn and detect relevant patterns in data, providing insight into the mechanisms behind neural network learning and enabling improvements in machine learning efficiency.
An article discussing the importance of explainability in machine learning and the challenges posed by neural networks. It highlights the difficulties in understanding the decision-making process of complex models and the need for more transparency in AI development.
This article explains the concept and use of Friedman's H-statistic for finding interactions in machine learning models.
Additive Decision Trees are a variation of standard decision trees, constructed in a way that can often allow them to be more accurate, more interpretable, or both. This article explains the intuition behind Additive Decision Trees and how they can be constructed.
Generating counterfactual explanations got a lot easier with CFNOW, but what are counterfactual explanations, and how can I use them?
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