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2. 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.

3. Resampling (statistics) - Wikipedia

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

   The best example of the plug-in principle, the bootstrapping method. Bootstrapping is a statistical method for estimating the sampling distribution of an estimator by sampling with replacement from the original sample, most often with the purpose of deriving robust estimates of standard errors and confidence intervals of a population parameter like a mean, median, proportion, odds ratio ...

4. 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 ...

5. Bootstrap (front-end framework) - Wikipedia

   en.wikipedia.org/wiki/Bootstrap_(front-end...

   Bootstrap (formerly Twitter Bootstrap) is a free and open-source CSS framework directed at responsive, mobile-first front-end web development. It contains HTML, CSS and (optionally) JavaScript -based design templates for typography, forms, buttons, navigation, and other interface components. As of May 2023, Bootstrap is the 17th most starred ...

6. Bootstrap aggregating - Wikipedia

   en.wikipedia.org/wiki/Bootstrap_aggregating

   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.

7. Bootstrapping (electronics) - Wikipedia

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

   In the field of electronics, a technique where part of the output of a system is used at startup can be described as bootstrapping. A bootstrap circuit is one where part of the output of an amplifier stage is applied to the input, so as to alter the input impedance of the amplifier. When applied deliberately, the intention is usually to ...

8. Horvitz–Thompson estimator - Wikipedia

   en.wikipedia.org/wiki/Horvitz–Thompson_estimator

   In statistics, the Horvitz–Thompson estimator, named after Daniel G. Horvitz and Donovan J. Thompson, is a method for estimating the total and mean of a pseudo-population in a stratified sample by applying inverse probability weighting to account for the difference in the sampling distribution between the collected data and the a target population.

9. Computational statistics - Wikipedia

   en.wikipedia.org/wiki/Computational_statistics

   The more steps are included, the more closely the distribution of the sample matches the actual desired distribution. Bootstrapping. The bootstrap is a resampling technique used to generate samples from an empirical probability distribution defined by an original sample of the population. It can be used to find a bootstrapped estimator of a ...