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Repeated Cross Validation in Bagging by Nosheen Ramzan is a document available to read on EtoBox.

What is Repeated Cross Validation in Bagging about?

Bagging is a technique to reduce variance in decision tree models by averaging multiple trees built on bootstrap samples of the original data. It works by 1) generating B bootstrap samples from the training data, 2) building a decision tree on each sample, and 3) averaging the predictions of the B trees. This reduces variance compared to a single tree. For regression, the average prediction is used, while for classification a majority vote of the tree predictions is taken. The out-of-bag error provides an e

Author

Nosheen Ramzan

Language

EN