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  2. Bootstrap aggregating - Wikipedia

    en.wikipedia.org/wiki/Bootstrap_aggregating

    v. t. e. Bootstrap aggregating, also called bagging (from b ootstrap agg regat ing ), 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.

  3. Ensemble learning - Wikipedia

    en.wikipedia.org/wiki/Ensemble_learning

    Bootstrap aggregation (bagging) involves training an ensemble on bootstrapped data sets. A bootstrapped set is created by selecting from original training data set with replacement. Thus, a bootstrap set may contain a given example zero, one, or multiple times.

  4. Bootstrapping (statistics) - Wikipedia

    en.wikipedia.org/wiki/Bootstrapping_(statistics)

    Bootstrapping is any test or metric that uses random sampling with replacement (e.g. mimicking the sampling process), and falls under the broader class of resampling methods. Bootstrapping assigns measures of accuracy ( bias, variance, confidence intervals, prediction error, etc.) to sample estimates.

  5. Random subspace method - Wikipedia

    en.wikipedia.org/wiki/Random_subspace_method

    Random subspace method. 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.

  6. Out-of-bag error - Wikipedia

    en.wikipedia.org/wiki/Out-of-bag_error

    One set, the bootstrap sample, is the data chosen to be "in-the-bag" by sampling with replacement. The out-of-bag set is all data not chosen in the sampling process. When this process is repeated, such as when building a random forest, many bootstrap samples and OOB sets are created. The OOB sets can be aggregated into one dataset, but each ...

  7. Leo Breiman - Wikipedia

    en.wikipedia.org/wiki/Leo_Breiman

    Leo Breiman. Leo Breiman (January 27, 1928 – July 5, 2005) was a distinguished statistician at the University of California, Berkeley. He was the recipient of numerous honors and awards, [citation needed] and was a member of the United States National Academy of Sciences . Breiman's work helped to bridge the gap between statistics and ...

  8. Bootstrapping (compilers) - Wikipedia

    en.wikipedia.org/wiki/Bootstrapping_(compilers)

    Bootstrapping (compilers) In computer science, bootstrapping is the technique for producing a self-compiling compiler – that is, a compiler (or assembler) written in the source programming language that it intends to compile. An initial core version of the compiler (the bootstrap compiler) is generated in a different language (which could be ...

  9. Talk:Bootstrapping (statistics) - Wikipedia

    en.wikipedia.org/wiki/Talk:Bootstrapping...

    Bootstrap methods are great for inference, but bootstrap aggregation is a method for ensemble learning - i.e. to aggregate collections of models, for robust development using subsamples of the data. To include bagging into bootstrapping is to misunderstand the use of bagging.