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What is resampling in machine learning?

Re-sampling is a series of methods used to reconstruct your sample data sets, including training sets and validation sets.

For example, in Random Forest Algorithm, we can assume that we only have a training set [math]\cal{D}[/math] with [math]N[/math] samples in it, and now we need to construct [math]T[/math] decision trees by iterating the learning process for [math]T[/math] times.

The aggregation of different trees learning from separately re-sampling training sets can achieve higher accuracy and avoid overfitting to some extent.

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