Bagging Linear Regression. When random subsets of the dataset are drawn as random subsets of the features then the method is known as Random Subspaces. Bagging trains each regression model ie base learner with the different training sets generated by sampling with replacement from the training data then it averages the predictions of each.
Ad Free online and interactive data science tutorials. As its name suggests bootstrap aggregation is based on the idea of the bootstrap sample. In this case heavily pruned Decision Trees which also have a lower bias are used.
Regression trees and subset selection in linear regression show that bagging can give substantial gains in accuracy.
When random subsets of the dataset are drawn as random subsets of the features then the method is known as Random Subspaces. Finally when base estimators are built on subsets of both samples and features then the method is known as Random Patches. Generating several samples from the original. A similar technique using bagging to learn a linear model is RANSAC regression except there are se.
