Understanding Bootstrap Aggregating (Bagging)

Gabriel Vasconcelos explains the bagging technique:

The name bagging comes from boostrap aggregating. It is a machine learning technique proposed by Breiman (1996) to increase stability in potentially unstable estimators. For example, suppose you want to run a regression with a few variables in two steps. First, you run the regression with all the variables in your data and select the significant ones. Second, you run a new regression using only the selected variables and compute the predictions.

This procedure is not wrong if your problem is forecasting. However, this two step estimation may result in highly unstable models. If many variables are important but individually their importance is small, you will probably leave some of them out, and small perturbations on the data may drastically change the results.

Read on to see how bootstrap aggregation works and how it solves this solution instability problem.

Related Posts

The Costs of Specialization within Data Science

Eric Colson argues in favor of data science generalists rather than specialists: But the goal of data science is not to execute. Rather, the goal is to learn and develop profound new business capabilities. Algorithmic products and services like recommendations systems, client engagement bandits, style preference classification, size matching, fashion design systems, logistics optimizers, seasonal trend detection, and more can’t be […]

Read More

Accidentally Building a Population Graph

Neil Saunders shares an example of a newspaper headline which ultimately just shows us population sizes: Some poking around in the NSW Transport Open Data portal reveals how many people enter every Sydney train station on a “typical” day in 2016, 2017 and 2018. We could manipulate those numbers in various ways to estimate total, unique […]

Read More

Categories