decision tree


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decision tree

[di′sizh·ən ‚trē]
(industrial engineering)
Graphic display of the underlying decision process involved in the introduction of a new product by a manufacturer.

decision tree

A graphical representation of all alternatives in a decision making process.
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People randomized to the decision tree group also did significantly better than the standard care group in three measures of attitudes about quitting (Figure 2): (1) a higher level of motivation to quit (8.
The thematic layers are transformed into features and these features are supplied as input to weighted decision tree prediction model.
Decision trees can be unstable and complex, and are dependent on being fed reliable information, (Dowding and Thompson, 2002), that is, information which is relevant and specific to the clinical population, setting and function of the decision tree.
Takashaki and Abe [24] proposed OAA SVM based decision tree formulation in literature to overcome the problem of unclassifiable region to improve generalization ability of SVM.
A more detailed description of decision trees than the other techniques follows because use of the decision tree technique is the main extension provided by this study.
All the traditional classifiers such as nearest neighbour (5), hand-crafted decision trees (8), neural networks (9) and SVMs (10) have been tried with varying levels of success.
After building a decision tree on T, it then produces a succession of smaller trees by looking at every non-leaf node in the tree, and measuring the cost complexity of that node.
The decision tree relates a posture of hierarchical collaboration to knowledge generated to meet a genre-based need.
Decision tree method as well as regression analysis showed that the operative costs / assets ratio and short-term credits / assets ratio were variables that had the biggest influence on net profit margin.
From decision trees, graduate student Tao Shi of the Department of Human Genetics at the University of California, Los Angeles, took the audience into the woods as he described the use of "random forest" predictors to derive information from microarray data.
The Decision Tree research was the culmination of several weeks of consumer buying observations in the meat aisles of five of the UK's leading supermarkets.

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