# Questions tagged [bagging]

Bagging or bootstrap aggregation is a special case of model averaging. Given a standard training set bagging generates $m$ new training sets by bootstrapping, and then the results of using some training method on the $m$ generated data sets are averaged. Bagging can stabilize results from some unstable methods such as trees.

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### Is there a model, apart from Random Forest, which uses bagging?

What are the popular machine learning algorithms which make use of bagging? Are there any apart from random forest?
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### Why can't we sample without replacement for each tree in a random forest if the subsample size is large enough?

Usually if we have $n$ observations, for each tree with form a bootstrapped subsample of size $n$ with replacement. On googling it one common explanation I've seen is that with replacement sampling is ...
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### Proof that bagging increases bias (estimation of squared mean)

We have independent and identically distributed training data $\mathcal{T} = \{z_{i}\}_{i=1}^{N}$, where $z_{i} = ( y_{i}, \mathbf{x}_{i})$, $y_{i} \in\mathbb{R}$. Further, suppose we want to estimate ...
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Since the original AdaBoost article it has been found out that boosting reduces both variance and bias in the classifier (in contrast to bagging, which only reduces variance). Original AdaBoost (and ...
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### Bagging vs pasting: bias-variance tradeoff

In the Hands-On ML with Scikit-Learn book, it states that, ...bagging ends up with a slightly higher bias than pasting, but... the ensemble's variance is reduced. I am a bit confused about this ...
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### Elements of Statistical Learning: Variance Reduction via Bagging for Random Forests

While Reading chapter 15 of ESL about Random Forests I had some confusion with this phrase: The idea in random forests (Algorithm 15.1) is to improve the variance reduction of bagging by reducing the ...
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### Regarding bagging, boosting and the NFL theorem

According to my understanding, bagging and boosting work in the following way: Bagging: Combine several high-variance/low-bias models to produce an ensemble model with lower variance and equal bias. ...
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### Why not always use ensemble learning?

It seems to me that ensemble learning WILL always give better predictive performance than with just a single learning hypothesis. So, why don't we use them all the time? My guess is because of ...
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### Do tree methods like gradient boosting predict all iterations at once?

If I'm using a tree method (e.g GBM) and I have a time series hourly data, and I predict my target variable $y$ for the next 48 hours, do my predictions were made all at once, or does the second day ...
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### How does bagging effect linear model assumptions?

Linear regression has model assumptions. How does bagging effect model assumptions for linear regression? Also, should you build a bagged linear model with correlated and statistically significant ...
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### Performing Bagging with the Lasso

I asked this question at StackOverflow but I think it might be better suited for here because I'm hoping to understand the general idea of bagging. I'm trying to implement bagging in ...
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### When should the Pasting ensemble method be used instead of Bagging?

Pasting and Bagging are very similar, the main difference being that Bagging samples with replacement (which is called "bootstrapping") while Pasting samples without replacement. I am guessing that ...
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### Bagging results from multiple k-fold cross-validation runs to estimate performance

Given a binary classification problem and a model construction algorithm, suppose I run, as an example, 5 fold (stratified) cross-validation 4 times. I can think of these as 5x4 = 20 training and 20 ...
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### Trying to improve the generalization ability of decision tree with bagging or random forest

We are fitting a regression tree on a sample of about 5000 observations with 5 predictors. The data stems from 2 sources. There are reasons to suspect that the tree will not have exaggerated ...