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Bootstrap Aggregating
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Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. 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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Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. 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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