Bagging
Bagging, short for Bootstrap Aggregating, is a machine learning ensemble meta-algorithm designed to improve the stability and accuracy of machine learning algorithms. It involves training multiple models on different subsets of the training data, using a random sampling with replacement technique called bootstrapping. The outputs of these models are then combined using a voting or averaging method to produce the final prediction. Bagging can be applied to a wide range of machine learning algorithms, including decision trees, neural networks, and support vector machines. It is particularly effective in reducing overfitting and improving the generalization performance of the model, especially when the training data is noisy or limited.
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