Bagging and Boosting

Two simple enhancements to tree-based methods are called bagging and boosting. These iterative schemes each produce a committee of expert tree models by resampling with replacement from the initial data set. Afterward, the expert tree models are averaged using a plurality voting scheme if the response is discrete, or simple averaging if the response is continuous. The difference between bagging and boosting is the way in which data are resampled. In the former, all observations have equal probability of entering the next bootstrap sample; in the latter, problematic observations (i.e., observations that have been frequently misclassified) have a higher probability of selection.

Was this article helpful?

0 0
10 Ways To Fight Off Cancer

10 Ways To Fight Off Cancer

Learning About 10 Ways Fight Off Cancer Can Have Amazing Benefits For Your Life The Best Tips On How To Keep This Killer At Bay Discovering that you or a loved one has cancer can be utterly terrifying. All the same, once you comprehend the causes of cancer and learn how to reverse those causes, you or your loved one may have more than a fighting chance of beating out cancer.

Get My Free Ebook


Post a comment