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Bootstrapping (statistics) Bootstrapping is a procedure for estimating the distribution of an estimator by resampling (often with replacement) one's data or a model estimated from the data. [1] Bootstrapping assigns measures of accuracy (bias, variance, confidence intervals, prediction error, etc.) to sample estimates. [2][3] This technique ...
Bootstrap error-adjusted single-sample technique. In statistics, the bootstrap error-adjusted single-sample technique (BEST or the BEAST) is a non-parametric method that is intended to allow an assessment to be made of the validity of a single sample. It is based on estimating a probability distribution representing what can be expected from ...
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.
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Snippet is a programming term for a small region of re-usable source code, machine code, or text. Ordinarily, these are formally defined operative units to incorporate into larger programming modules. Snippet management is a feature of some text editors, program source code editors, IDEs, and related software.
e. Cascading Style Sheets (CSS) is a style sheet language used for specifying the presentation and styling of a document written in a markup language such as HTML or XML (including XML dialects such as SVG, MathML or XHTML). [1] CSS is a cornerstone technology of the World Wide Web, alongside HTML and JavaScript.
The data URI scheme is a uniform resource identifier (URI) scheme that provides a way to include data in-line in Web pages as if they were external resources. It is a form of file literal or here document. This technique allows normally separate elements such as images and style sheets to be fetched in a single Hypertext Transfer Protocol (HTTP ...
A standard test image is a digital image file used across different institutions to test image processing and image compression algorithms. By using the same standard test images, different labs are able to compare results, both visually and quantitatively. The images are in many cases chosen to represent natural or typical images that a class ...