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.
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Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)
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© 2003 Springer-Verlag Berlin Heidelberg
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Breiman, L. (2003). Two-Eyed Algorithms and Problems. In: Lavrač, N., Gamberger, D., Todorovski, L., Blockeel, H. (eds) Knowledge Discovery in Databases: PKDD 2003. PKDD 2003. Lecture Notes in Computer Science(), vol 2838. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39804-2_2
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DOI: https://doi.org/10.1007/978-3-540-39804-2_2
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