Bagging
Learn how to perform bagging using scikit-learn.
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Overview
Bagging is a method designed to diminish the variance of an estimator. This is accomplished by training numerous models on distinct subsets of the training data. Each of these subsets is employed to train an individual base learner, and the outcomes from these learners are then aggregated through a voting or averaging process.
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Bagging is a building block for many ensemble methods, including the famous random forest algorithm. It’s a robust tool for improving the generalization performance of machine learning models, making it a valuable asset in a data scientist’s toolbox.
Steps
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