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Each Bootstrap component consists of an HTML structure, CSS declarations, and in some cases accompanying JavaScript code. They also extend the functionality of some existing interface elements, including for example an auto-complete function for input fields. Example of a webpage using Bootstrap framework rendered in Firefox
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.
In computer technology, the term bootstrapping refers to language compilers that are able to be coded in the same language. (For example, a C compiler is now written in the C language. Once the basic compiler is written, improvements can be iteratively made, thus pulling the language up by its bootstraps).
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 ...
Bootloader. GNU GRUB, a popular open source bootloader. Windows bootloader. A bootloader, also spelled as boot loader [1] [2] or called bootstrap loader, is a computer program that is responsible for booting a computer. If it also provides an interactive menu with multiple boot choices then it's often called a boot manager.
Each color code listed is a shorthand for the RGB value. For example, code 609 is equivalent to RGB code 102-0-153 or HEX code #660099. Safest web colors. Designers were encouraged [by whom?] to stick to these 216 "web-safe" colors in their websites because there were a lot of 8-bit color displays when the 216-color palette was developed.
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.
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