Bagging
Explore the bagging technique in ensemble learning to reduce model variance and improve prediction stability. This lesson helps you understand the process of creating diverse training subsets, independently training base learners like decision trees, and combining their outputs through voting or averaging. Gain practical insights by implementing bagging using scikit-learn, enhancing your ability to apply ensemble methods in real-world machine learning projects.
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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.
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
Random sampling: Bagging randomly selects subsets of the training data with replacement, allowing the same instance to appear in multiple subsets. This process introduces diversity among the base learners.
Parallel training: Base learners in bagging are trained independently and in parallel, which makes it suitable for parallel and distributed computing environments.
Voting/averaging: The predictions of individual ...