Search results
Results from the WOW.Com Content Network
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 ...
Solution: divide one of the tall cells so that the row gets one rowspan=1 cell (and don't mind the eventual loss of text-centering). Then kill the border between them. Don't forget to fill the cell with nothing ({}). This being the only solution that correctly preserves the cell height, matching that of the reference seven row table.
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).
Can be placed at the beginning of article sections that need expansion, after the section title. Template parameters [Edit template data] This template prefers inline formatting of parameters. Parameter Description Type Status Reason 1 with for Reason the template was added, an explanation of what expansion the section needs. A bulleted list with lines beginning '*' can be given. Content ...
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.
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 ...
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 ...