Bagging, also called bootstrap aggregating, comes in a few flavors and these are defined by the way they draw random subsets from the training data. Most commonly, bagging refers to drawing samples with replacement. Because the samples are replaced, it is possible for the generated datasets to contain duplicates. It also means that data points may be excluded from a particular generated dataset, even if this generated set is the same size as the original. Each of the generated datasets will be different and this is a way to create diversity among the models in an ensemble. We can calculate the probability that a data point is not selected in a sample using the following example:
Here, n is the number of bootstrap samples. Each of the n bootstrap samples results in a different hypothesis. The class is predicted either by averaging the models or by choosing the class predicted by the majority of models. Consider an ensemble of linear classifiers. If we use majority voting to determine...
