| Bootstrap Aggregating |
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| CATEGORIES ABOUT BOOTSTRAP AGGREGATING | |
| ensemble learning | |
| machine learning | |
| computational statistics | |
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Given a standard Training Set ''D'' of size ''N'', we generate ''L'' new training sets also of size ''N'' by sampling examples uniformly from ''D'', and with replacement. By sampling with replacement it is likely that some examples will be repeated in each . On average the set will have 63.2% of the examples of ''D'', the rest being duplicates. This kind of sample is known as a bootstrap sample. The ''L'' models are fitted using the above ''L'' bootstrap samples and combined by averaging the output (in case of regression) or voting (in case of classification). REFERENCES Leo Breiman. Bagging predictors. Machine Learning, 24(2):123140, 1996. SEE ALSO |
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