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2. 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 ...

3. 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.

4. 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.

5. 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 ...

6. Bootstrap Protocol - Wikipedia

   en.wikipedia.org/wiki/Bootstrap_Protocol

   t. e. The Bootstrap Protocol ( BOOTP) is a computer networking protocol used in Internet Protocol networks to automatically assign an IP address to network devices from a configuration server. The BOOTP was originally defined in RFC 951 published in 1985. While some parts of BOOTP have been effectively superseded by the Dynamic Host ...

7. Homoscedasticity and heteroscedasticity - Wikipedia

   en.wikipedia.org/wiki/Homoscedasticity_and...

   For any non-linear model (for instance Logit and Probit models), however, heteroscedasticity has more severe consequences: the maximum likelihood estimates (MLE) of the parameters will usually be biased, as well as inconsistent (unless the likelihood function is modified to correctly take into account the precise form of heteroscedasticity or ...