Slow-Growing Trees
This work provides a unifying view of tree-based methods, offering a simpler alternative to ensembles for practitioners in machine learning.
The paper tackles the problem of matching Random Forest performance using a single slow-growing tree (SGT) that applies a learning rate to control CART's greediness, achieving comparable results on simulated and real regression tasks.
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.