Slow-Growing Trees
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 11 of its 49 references that are in the map. First posted on arXiv 2 Mar 2021.
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11 of 49 references.
References · 11
- 2018Reconciling modern machine learning practice and the bias-variance trade-offcited by 919
- 2019Benign Overfitting in Linear Regressioncited by 544
- 2019Optimal Sparse Decision Treescited by 104
- 2018Optimal ridge penalty for real-world high-dimensional data can be zero or negative due to the implicit ridge regularizationcited by 52
- 2015Explaining the Success of AdaBoost and Random Forests as Interpolating Classifierscited by 45
- 2020How is Machine Learning Useful for Macroeconomic Forecasting?cited by 18
- 2015Efficient non-greedy optimization of decision treescited by 17
- 2020The Macroeconomy as a Random Forestcited by 15
- 2020Macroeconomic Data Transformations Mattercited by 7
- 2021Can Machine Learning Catch the COVID-19 Recession?cited by 6
- 2020To Bag is to Prunecited by 5
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