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Model Compression via ISLE and RuleLearner

This guide elaborates on powerful ways to combine the TreeNet and GPS engines to achieve model compression and more.

The principal idea behind model compression is that it may be possible to use modern regression techniques to post-process a complex model to create a radically simplified version that can perform almost as well as the original. It can go even further: if you build the original complex model in a specific way there is an excellent chance that the post-processed simplified model will perform even better than the best possible complex model!.

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