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20052026
most citedAll Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously

1.2k citations · 5k across the 152 of their papers we have counts for

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Showing 2008 · stat.MLShow all

2 papers · 2 filters

stat.ML2008★ 4 cited

Online Coordinate Boosting

Raphael Pelossof, Michael Jones, Ilia Vovsha +1

We present a new online boosting algorithm for adapting the weights of a boosted classifier, which yields a closer approximation to Freund and Schapire's AdaBoost algorithm than pr…

stat.ML2008★ 30 cited

Analysis of boosting algorithms using the smooth margin function

Cynthia Rudin, Robert E. Schapire, Ingrid Daubechies

We introduce a useful tool for analyzing boosting algorithms called the ``smooth margin function,'' a differentiable approximation of the usual margin for boosting algorithms. We p…