Machine learning - Bootstrap aggregating (bagging)
Table of Contents
1 - About
Bootstrap aggregating (bagging) is a machine learning ensemble meta-algorithm designed to improve the stability and accuracy of machine learning algorithms used in statistical classification and regression.
- produces several different training sets of the same size with replacement
- and then build a model for each one using the same machine learning scheme
- Combine predictions by voting for a nominal target or averaging for a numeric target
Bagging can be parallelized.
2 - Articles Related
3 - Advantage / Inconvenient
It's very suitable for “unstable” learning schemes which means that small change in training data can make big change in the model.
4 - Replacement
In bagging, you sample the set “with replacement” which means that you might get in your sample two of the same instance.
5 - Implementation
5.1 - Weka