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Accordion widget. The accordion is a graphical control element comprising a vertically stacked list of items, such as labels or thumbnails. Each item can be "expanded" or "collapsed" to reveal the content associated with that item. There can be zero expanded items, exactly one, or more than one item expanded at a time, depending on the ...
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 ...
A collapsible element contains a toggle a reader can use to show or hide the element's content. Elements are made collapsible by adding the mw-collapsible class, or alternatively by using the {{}} template, or its variants {{Collapse top}} and {{Collapse bottom}}.
In general, bootstrapping usually refers to a self-starting process that is supposed to continue or grow without external input. Many analytical techniques are often called bootstrap methods in reference to their self-starting or self-supporting implementation, such as bootstrapping (statistics), bootstrapping (finance), or bootstrapping (linguistics).
At this point, all further development is done using the language defined by the bootstrap compiler, and stage 2 begins. Stage 2: a full compiler is produced by the bootstrap compiler. This is typically done in stages as needed, e.g. compiler for version X of the language will be able to compile features from version X+1, but that compiler does ...
Bootstrap aggregating, also called bagging (from bootstrap aggregating) or bootstrapping, is a machine learning (ML) ensemble meta-algorithm designed to improve the stability and accuracy of ML classification and regression algorithms.
A mesh need not be simplicial because an arbitrary subset of nodes of a cell is not necessarily a cell: e.g., three nodes of a quad does not define a cell. However, two cells intersect at cells: e.g. a quad does not have a node in its interior. The intersection of two cells may be several cells: e.g., two quads may share two edges.
The bootstrap sample is taken from the original by using sampling with replacement (e.g. we might 'resample' 5 times from [1,2,3,4,5] and get [2,5,4,4,1]), so, assuming N is sufficiently large, for all practical purposes there is virtually zero probability that it will be identical to the original "real" sample. This process is repeated a large ...