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- 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
- machine learning - What is the difference between bagging and random . . .
" The fundamental difference between bagging and random forest is that in Random forests, only a subset of features are selected at random out of the total and the best split feature from the subse
- 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 on average does each bootstrap sample contain roughly two thirds of . . .
I have run across the assertion that each bootstrap sample (or bagged tree) will contain on average approximately $2 3$ of the observations I understand that the chance of not being selected in
- 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
- machine learning - Why does creating training sets with replacement . . .
Pasting generally suffers from lower performance than bagging, because it training sets without replacement Why does creating training sets with replacement lead to better performance?
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