I don't know much about boosting, but I accidentally noticed that we have the following boosting-related tags:
- boosting × 311
- gbm × 179
- gradient-boosted-trees × 28
The problem is that boosting (https://en.wikipedia.org/wiki/Gradient_boosting) is almost always used with decision trees, so [boosting] is almost exclusively about [gradient-boosted-trees]. Also, "GBM" stands for "gradient boosted machine" which was in the title of Friedman's famous 1999 paper... where he introduced boosted trees! So it's all the same thing.
Therefore, I think [gradient-boosted-trees] and [gbm] should become synonyms of [boosting]. This combined tag would have 486 threads. This seems to me to be a pretty clear-cut case.
In addition, we could consider merging the following tags:
into [boosting] as well. These tags are not that large, are clear subsets of [boosting], and whoever is using boosting in practice is most likely (?) using either xgboost
or adaboost
anyway. If we merge all the above-listed tags into [boosting], then it would have 613 threads. I am making this second suggestion because I generally prefer larger tags, but I don't have much of an informed opinion here.
GradientBoostingClassifier
/GradientBoostingRegressor
implementations inscikit-learn
, which are not xgboost. And my impression is that while adaboost is still learned about a lot because of its historical significance, it may not be used that much in practice anymore. $\endgroup$