Bootstrap aggregating [SEP] discoverer or inventor
100 smoothers, each fitted to a subset of the original data set, we arrive at one bagged predictor (red line). Clearly, the mean is more stable and there is less overfit.
History.
Bagging (Bootstrap aggregating) was proposed by Leo Breiman in 1994 to improve classification by combining classifications of randomly generated training sets.
See also.
- Boosting (meta-algorithm)
- Bootstrapping (statistics)
- Cross-validation (statistics)
- Random forest
- Random subspace
Bootstrap aggregating
Bootstrap aggregating, also called 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. It also reduces variance and helps to avoid overfitting. Although it is usually applied to decision tree methods, it can be used with any type of method. Bagging is a special case of the model averaging approach.
Description of the technique.
Given a standard training set formula_1 of size "n", bagging generates "m