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Decision Tree Model. In computational complexity theory, the decision tree model is the model of computation in which an algorithm can be considered to be a decision tree, i.e. a sequence of queries or tests that are done adaptively, so the outcome of previous tests can influence the tests performed next.
Microsoft Word is a word processing program developed by Microsoft.It was first released on October 25, 1983, [11] under the name Multi-Tool Word for Xenix systems. [12] [13] [14] Subsequent versions were later written for several other platforms including: IBM PCs running DOS (1983), Apple Macintosh running the Classic Mac OS (1985), AT&T UNIX PC (1985), Atari ST (1988), OS/2 (1989 ...
A decision tree is a decision support recursive partitioning structure that uses a tree-like model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. It is one way to display an algorithm that only contains conditional control statements.
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Business decision mapping (BDM) is a technique for making decisions, particularly for the kind of decisions that often need to be made in business.It involves using diagrams to help articulate and work through the decision problem, from initial recognition of the need through to communication of the decision and the thinking behind it.
Microsoft Office 2010 (codenamed Office 14 [6]) is a version of Microsoft Office for Microsoft Windows unveiled by Microsoft on May 15, 2009, and released to manufacturing on April 15, 2010, [1] with general availability on June 15, 2010. [7] The macOS equivalent, Microsoft Office 2011 for Mac was released on October 26, 2010.
Fast-and-frugal tree or matching heuristic [1] (in the study of decision-making) is a simple graphical structure that categorizes objects by asking one question at a time. These decision trees are used in a range of fields: psychology , artificial intelligence , and management science .
LightGBM, short for Light Gradient-Boosting Machine, is a free and open-source distributed gradient-boosting framework for machine learning, originally developed by Microsoft. [4] [5] It is based on decision tree algorithms and used for ranking, classification and other machine learning tasks. The development focus is on performance and ...