Bagging
Explore the bagging technique to understand how training several models on bootstrapped data subsets and aggregating their outputs can reduce variance and improve prediction accuracy. This lesson guides you through its implementation using decision trees and scikit-learn, enhancing your ability to build robust ensemble models.
We'll cover the following...
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 base learners are combined ...