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- Bagging, boosting and stacking in machine learning
Bagging should be used with unstable classifiers, that is, classifiers that are sensitive to variations in the training set such as Decision Trees and Perceptrons Random Subspace is an interesting similar approach that uses variations in the features instead of variations in the samples, usually indicated on datasets with multiple dimensions
- bagging - Why do we use random sample with replacement while . . .
First, definitorial answer: Since "bagging" means "bootstrap aggregation", you have to bootstrap, which is defined as sampling with replacement Second, more interesting: Averaging predictors only improves the prediction if they are not overly correlated The replacement reduces similarity of data, and hence correlation of predictions
- machine learning - What is the difference between bagging and random . . .
Bagging (bootstrap + aggregating) is using an ensemble of models where: each model uses a bootstrapped data set (bootstrap part of bagging) models' predictions are aggregated (aggregation part of bagging) This means that in bagging, you can use any model of your choice, not only trees Further, bagged trees are bagged ensembles where each model
- Is it pointless to use Bagging with nearest neighbor classifiers . . .
On the other hand, stable learners (take to the extreme a constant), will give quite similar predictions anyway so bagging won't help He also refer to specific algorithms stability: Unstability was studied in Breiman [1994] where it was pointed out that neural nets, classi cation and regression trees, and subset selection in linear regression
- How is bagging different from cross-validation?
Bagging uses bootstrapped subsets (i e drawing with replacement of the original data set) of training data to generate such an ensemble but you can also use ensembles that are produced by drawing without replacement, i e cross validation: Beleites, C Salzer, R : Assessing and improving the stability of chemometric models in small sample
- Subset Differences between Bagging, Random Forest, Boosting?
Bagging draws a bootstrap sample of the data (randomly select a new sample with replacement from the existing data), and the results of these random samples are aggregated (because the trees' predictions are averaged) But bagging, and column subsampling can be applied more broadly than just random forest
- When can bagging actually lead to higher variance?
I assume that we compare the variance of an ensemble estimator (e g bagging) against that of a well-calibrated "single" predictor trained on the full training set While in the context of generating predictions, bagging is known to reduce the variance, I see two main ways that can lead to higher variance in predictions: We do improper aggregation
- machine learning - How can we explain the fact that Bagging reduces . . .
Since only the variance can be reduced, decision trees are build to node purity in context of random forest and tree bagging (Building to node purity maximizes the variance of the individual trees, i e they fit the data perfectly, while minimizing the bias )
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