Explanation of hyperparameter tuning procedure for regression tree ensembles
35 次查看(过去 30 天)
显示 更早的评论
What regression tree ensemble methods and what parameters does Matlab actually consider in hyperparameter tuning?
See https://se.mathworks.com/help/stats/fitrensemble.html and the example "Optimize Regression Ensemble" therein. It says "You can find hyperparameters that minimize five-fold cross-validation loss by using automatic hyperparameter optimization."
But what is the search space here?
The output in that example only displays Bag and LSBoost as methods. Does it neglect random forests, i.e. subset sampling instead of bootstrapping the input space? Or is bagging here an umbrella term that covers also Random Forests?
Furthermore, the output in the above example only displays NumLearnCycles (tree count), LearnRate (for boosting) and MinLeafSize (obvious). How about treatment of the other CART decision tree algorithm hyperparameters? Are they included as default values - if so, then where to find them?
0 个评论
回答(1 个)
Alan Weiss
2021-3-15
You can find all the information later on in that same reference page:
Alan Weiss
MATLAB mathematical toolbox documentation
0 个评论
另请参阅
类别
在 Help Center 和 File Exchange 中查找有关 Regression 的更多信息
产品
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!