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In user interface design for computer applications, a modal window is a graphical control element subordinate to an application's main window . A modal window creates a mode that disables user interaction with the main window but keeps it visible, with the modal window as a child window in front of it. Users must interact with the modal window ...
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 ...
Non-modal or modeless dialog boxes are used when the requested information is not essential to continue, and so the window can be left open while work continues elsewhere. A type of modeless dialog box is a toolbar which is either separate from the main application, or may be detached from the main application, and items in the toolbar can be used to select certain features or functions of the ...
Mode (user interface) In user interface design, a mode is a distinct setting within a computer program or any physical machine interface, in which the same user input will produce perceived results different from those that it would in other settings. Modal interface components include the Caps lock and Insert keys on the standard computer ...
For the music theory concept of "modes", see Mode (music). In statistics, the mode is the value that appears most often in a set of data values. [ 1 ] If X is a discrete random variable, the mode is the value x at which the probability mass function takes its maximum value (i.e., x=argmaxxi P (X = xi) ). In other words, it is the value that is ...
In statistics, a multimodal distribution is a probability distribution with more than one mode (i.e., more than one local peak of the distribution). These appear as distinct peaks (local maxima) in the probability density function, as shown in Figures 1 and 2. Categorical, continuous, and discrete data can all form multimodal distributions.
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.
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 ...