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

Probabilities And Poker

Steve Miller has a notebook on 5-card draw probabilities: The population of 5 card draw hands, consisting of 52 choose 5 or 2598960 elements, is pretty straightforward both mathematically and statistically. So of course ever the geek, I just had to attempt to show her how probability and statistics converge. In addition to explaining the […]

Read More

There Is No Easy Button With Predictive Analytics

Scott Mutchler dispels some myths: There are a couple of myths that I see more an more these days.  Like many myths they seem plausible on the surface but experienced data scientist know that the reality is more nuanced (and sadly requires more work). Myths: Deep learning (or Cognitive Analytics) is an easy button.  You […]

Read More