The following example is a small classification dataset of fruits, based on their physical appearance. Feature subsampling can highlight different aspects of the dataset that might go unnoticed if overpowered by more prominent features. When the algorithm decides on a split during tree building, only a random sample of features or columns, without replacement, is considered. In this bootstrapped dataset, a given sample, or row, of the training data can exist multiple times due to replacement. With bagging (bootstrap aggregation), each decision tree is trained upon a different sample, with the replacement of the original dataset. Additionally, random forests use the techniques of bagging and feature subsampling to make sure that no two resulting decision trees are the same. The main idea behind random forests is to learn multiple independent decision trees and use a consensus method to predict the unknown samples. Example random forest with three decision trees.
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