Bootstrap aggregating (bagging) is a machine learning ensemble meta-algorithm to improve machine learning of classification and regression models in terms of stability and classification accuracy. It also reduces variance and helps to avoid overfitting. Although it is usually applied to decision tree models, it can be used with any type of model. Bagging is a special case of the model averaging approach.
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- Bootstrap aggregating (bagging) is a machine learning ensemble meta-algorithm to improve machine learning of classification and regression models in terms of stability and classification accuracy. It also reduces variance and helps to avoid overfitting. Although it is usually applied to decision tree models, it can be used with any type of model. Bagging is a special case of the model averaging approach. Given a standard training set D of size n, bagging generates m new training sets <math>D_i</math> of size n' ≤ n, by sampling examples from D uniformly and with replacement. By sampling with replacement it is likely that some examples will be repeated in each <math>D_i</math>. If n'=n, then for large n the set <math>D_i</math> expected to have 63.2% of the unique examples of D, the rest being duplicates. This kind of sample is known as a bootstrap sample. The m models are fitted using the above m bootstrap samples and combined by averaging the output (for regression) or voting (for classification). Since the method averages several predictors, it is not useful for improving linear models.
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- Bootstrap aggregating (bagging) is a machine learning ensemble meta-algorithm to improve machine learning of classification and regression models in terms of stability and classification accuracy. It also reduces variance and helps to avoid overfitting. Although it is usually applied to decision tree models, it can be used with any type of model. Bagging is a special case of the model averaging approach.
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