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FIGS: Attaining XGBoost-level performance with the interpretability and speed of CART
June 30, 2022, 9 a.m. |
The Berkeley Artificial Intelligence Research Blog bair.berkeley.edu
FIGS (Fast Interpretable Greedy-tree Sums): A method for building interpretable models by simultaneously growing an ensemble of decision trees in competition with one another.
Recent machine-learning advances have led to increasingly complex predictive models, often at the cost of interpretability. We often need interpretability, particularly in high-stakes applications such as in clinical decision-making; interpretable models help with all kinds of things, such as identifying errors, leveraging domain knowledge, and making speedy predictions.
In this blog post we’ll cover FIGS, …
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