Random subspace method
Method in machine learning / From Wikipedia, the free encyclopedia
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In machine learning the random subspace method,[1] also called attribute bagging[2] or feature bagging, is an ensemble learning method that attempts to reduce the correlation between estimators in an ensemble by training them on random samples of features instead of the entire feature set.