arXiv 2 Mar 2021 · Statistics — Machine Learning · 1 citations (OpenAlex)
arXiv:2103.01926 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Goulet Coulombe, P (2020) To bag is to prune | 1.000 | 8 | 3 | 100% |
| 2 | Rosset, S., Zhu, J., and Hastie, T (2004) Boosting as a regularized path to a maximum margin classifier | 1.000 | 6 | 3 | 100% |
| 3 | Friedman, J. H (2001) Greedy function approximation: a gradient boosting machine | 0.928 | 4 | 3 | 100% |
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| 5 | Bertsimas, D. and Dunn, J (2017) Optimal classification trees | 0.737 | 3 | 2 | 100% |
| 6 | Blanquero, R., Carrizosa, E., Molero-Ro, C., and Morales, D. R (2020) On sparse optimal regression trees | 0.737 | 3 | 2 | 100% |
| 7 | Irsoy, O., Yldz, O. T., and Alpaydn, E (2012) Soft decision trees | 0.737 | 3 | 2 | 100% |
| 8 | Hastie, T., Taylor, J., Tibshirani, R., Walther, G., et al (2007) Forward stagewise regression and the monotone lasso | 0.644 | 4 | 1 | 100% |
| 9 | Goulet Coulombe, P., Leroux, M., Stevanovic, D., Surprenant, S., et al (2019) How is machine learning useful for macroeconomic forecasting? | 0.644 | 2 | 2 | 100% |
| 10 | Chen, J. C., Dunn, A., Hood, K. K., Driessen, A., and Batch, A (2019) Off to the races: A comparison of machine learning and alternative data for predicting economic indicators | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 48 scored citations.