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

Bayesian Average

Jelte Hoekstra has a fun post applying the Bayesian average to board game ratings: Maybe you want to explore the best boardgames but instead you find the top 100 filled with 10/10 scores. Experience many such false positives and you will lose faith in the rating system. Let’s be clear this isn’t exactly incidental either: […]

Read More

Calculating Relative Risk In T-SQL

Mala Mahadevan explains how to calculate relative risk using T-SQL: In this post we will explore a common statistical term – Relative Risk, otherwise called Risk Factor. Relative Risk is a term that is important to understand when you are doing comparative studies of two groups that are different in some specific way. The most […]

Read More