I apologize for the delay in the answer to your last email. However, in response to him, we developed further experiments with GBM (using only two-class data sets) achieving good results, even better than random forest but only for two-class data sets. That comment has been issued by other researcher (David Herrington), our response was that we tried GBM (gradient boosting machine) in R directly and via caret, but we achieved errors for problems with more than two data sets. Here’s the answer, reprinted with permission: revisited the topic with the paper titled Do we need hundreds of classifiers to solve real world classification problems? Notably, there were no results for gradient-boosted trees, so we asked the author about it. Above that, random forests have the best overall performance. In this study boosted trees are the method of choice for up to about 4000 dimensions. First, the results confirm the experiments in (Caruana & Niculescu-Mizil, 2006) where boosted decision trees perform exceptionally well when dimensionality is low. In the follow-up study concerning supervised learning in high dimensions the results are similar:Īlthough there is substantial variability in performance across problems and metrics in our experiments, we can discern several interesting results. Second, it’s unclear what boosting method the authors used. First, they mention calibrated boosted trees, meaning that for probabilistic classification trees needed calibration to be the best. With excellent performance on all eight metrics, calibrated boosted trees were the best learning algorithm overall. They included random forests and boosted decision trees and concluded that made an empirical comparison of supervised learning algorithms. Let’s look at what the literature says about how these two methods compare. Folks know that gradient-boosted trees generally perform better than a random forest, although there is a price for that: GBT have a few hyperparams to tune, while random forest is practically tuning-free.
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