Bagging, boosting and stacking in machine learning What's the similarities and differences between these 3 methods: Bagging, Boosting, Stacking? Which is the best one? And why? Can you give me an example for each?
Is random forest a boosting algorithm? - Cross Validated A random forest, in contrast, is an ensemble bagging or averaging method that aims to reduce the variance of individual trees by randomly selecting (and thus de-correlating) many trees from the dataset, and averaging them
Bagging vs pasting: bias-variance tradeoff - Cross Validated Wouldn't bagging have higher variance and lower bias, since the sampled instances will be more correlated with each other compared to pasting? (Similar to how leave-one-out CV has higher variance due to higher correlation compared to K-fold )
Why does a bagged tree random forest tree have higher bias than a . . . Both Bagging and Random Forests use Bootstrap sampling, and as described in "Elements of Statistical Learning", this increases bias in the single tree Furthermore, as the Random Forest method limits the allowed variables to split on in each node, the bias for a single random forest tree is increased even more
machine learning - K-fold cross-bagging? - Cross Validated Edit: As frequently happens, a linked "related" question provides some insight: A comment on this question links to this paper, which argues -- in the context of many many bootstrap samples -- that one should select the level of the hyperparameter using cross-validation, and given that hyperparameter go back and re-do the bagging
Boosting AND Bagging Trees (XGBoost, LightGBM) Both XGBoost and LightGBM have params that allow for bagging The application is not Bagging OR Boosting (which is what every blog post talks about), but Bagging AND Boosting What is the pseudo code for where and when the combined bagging and boosting takes place? I expected it to be "Bagged Boosted Trees", but it seems it is "Boosted Bagged