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Two-Eyed Algorithms and Problems

  • Leo Breiman
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2837)

Abstract

Two-eyed algorithms are complex prediction algorithms that give accurate predictions and also give important insights into the structure of the data the algorithm is processing. The main example I discuss is RF/tools, a collection of algorithms for classification, regression and multiple dependent outputs. The last algorithm is a preliminary version and further progress depends on solving some fascinating questions of the characterization of dependency between variables.

An important and intriguing aspect of the classification version of RF/tools is that it can be used to analyze unsupervised data–that is, data without class labels. This conversion leads to such by-products as clustering, outlier detection, and replacement of missing data for unsupervised data.

The talk will present numerous results on real data sets. The code (f77) and ample documentation for RFtools is available on the web site www.stat.berkeley.edu/RFtools.

References

  1. 1.
    Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Leo Breiman
    • 1
  1. 1.Department of StatisticsUniversity of CaliforniaBerkeley

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