Slow-Growing Trees

Philippe Goulet Coulombe
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Philippe Goulet Coulombe: University of Pennsylvania

No 21-02, Working Papers from Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management

Abstract: Random Forest's performance can be matched by a single slow-growing tree (SGT), which uses a learning rate to tame CART's greedy algorithm. SGT exploits the view that CART is an extreme case of an iterative weighted least square procedure. Moreover, a unifying view of Boosted Trees (BT) and Random Forests (RF) is presented. Greedy ML algorithms' outcomes can be improved using either "slow learning" or diversification. SGT applies the former to estimate a single deep tree, and Booging (bagging stochastic BT with a high learning rate) uses the latter with additive shallow trees. The performance of this tree ensemble quaternity (Booging, BT, SGT, RF) is assessed on simulated and real regression tasks.

Pages: 12 pages
Date: 2021-03
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Persistent link: https://EconPapers.repec.org/RePEc:bbh:wpaper:21-02

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