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UID:submissions.supercomputing.org_SC24_sess533_post258@linklings.com
SUMMARY:PINE: Efficient Yet Effective Piecewise Linear Trees
DESCRIPTION:Zecheng Li and Jiajia Li (North Carolina State University)\n\n
 Decision trees are popularly used in statistics and machine learning. Piec
 ewise linear trees, a type of model-based decision tree, employ linear mod
 els to evaluate splits and predict outcomes at the leaf nodes. While they 
 can offer high accuracy, they are computationally expensive, and currently
 , no scalable implementations exist without harming accuracy. \n\nWe intro
 duce PINE, an efficient yet effective approach for training piecewise line
 ar trees, incorporating various algorithmic and system optimizations. Thes
 e optimizations enable fast training on multicore CPUs without sacrificing
  model accuracy. We also present PINEBoost, which applies gradient boostin
 g to PINE, and compare its performance with existing frameworks. Experimen
 tal results demonstrate that PINE and PINEBoost achieve superior accuracy 
 and faster convergence rates across general datasets in regression tasks c
 ompared to state-of-the-art gradient boosting decision trees.\n\nRegistrat
 ion Category: Tech Program Reg Pass, Exhibits Reg Pass\n\nSession Chairs: 
 Ayesha Afzal (Friedrich-Alexander University, Erlangen-Nuremberg; Erlangen
  National High Performance Computing Center); Sally Ellingson (University 
 of Kentucky); and Alan Sussman (University of Maryland)\n\n
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