Bootstrap aggregating, also known as bagging, is a meta-algorithm in machine learning that aims to enhance stability and accuracy in predictions. It achieves this by combining multiple models trained on different subsets of the training data. Each model is trained on a random sample, with replacement, from the original dataset.
The bagging process involves creating a diverse set of models that are then aggregated to make predictions. Each model is trained independently, utilizing a different subset of the data. By randomly selecting subsets with replacement, the training data for each model contains both repeated and missing instances, which promotes diversity among the models.
During the prediction phase, the outputs of all the individual models are combined to produce a final prediction. This combination can be done through voting, averaging, or other methods, depending on the problem at hand.
The strength of bagging lies in its ability to reduce overfitting and improve the stability of predictions. By training multiple models on different subsets of the data, bagging effectively reduces the impact of outliers or noisy data points. It also helps to capture different aspects of the underlying data distribution, leading to a more robust and accurate prediction.
In conclusion, bagging is a powerful technique that leverages the diversity of multiple models to enhance the stability and accuracy of predictions in machine learning tasks. Automatic Bag Filling Machine
"What is Bagging? A Comprehensive Tutorial on Ensemble Method - Tutorial 42"
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