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

DBA Salary Calculations

Eugene Meidinger takes a whack at the data professional salary survey: So I’m using something called a multiple linear regression to make a formula to predict your salary based on specific variables. Unfortunately, the highest Coefficient of Determination (or R2) I’ve been able to get is 0.37. Which means, as far as I understand it, that at most the […]

Read More

Choose Your Own Regression Adventure

Jim Frost explains when you might use different types of regression analysis: Regression analysis mathematically describes the relationship between a set of independent variables and a dependent variable. There are numerous types of regression models that you can use. This choice often depends on the kind of data you have for the dependent variable and the type of model […]

Read More

Categories