decision tree

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de·ci·sion tree

(dē-sizh'ŭn trē),
A graphic construct showing available choices at each decision node of managing a clinical problem along with probabilities (if known) of possible outcomes for patient's freedom from disability, life expectancy, and mortality.

decision tree

Decision-making A schematic representation of the major steps taken in a clinical decision algorithm; a DT begins with the statement of a clinical problem that can be followed along branches, based on the presence or absence of certain objective features, and eventually arrive at a conclusion

de·ci·sion tree

(dĕ-sizh'ŭn trē)
A graphic construct showing available choices at each decision node of managing a clinical problem along with probabilities (if known) of possible outcomes for patient's freedom from disability, life expectancy, and mortality.

de·ci·sion tree

(dĕ-sizh'ŭn trē)
A graphic construct showing available choices at each decision node of managing a clinical problem along with probabilities (if known) of possible outcomes for patient's freedom from disability, life expectancy, and mortality.
References in periodicals archive ?
Decision Tree Algorithms provide an effective method of Decision Making because it clearly lays out the problem so that all options can be challenged.
In data mining, decision tree method has widespread use in many scientific areas, such as agriculture, engineering and industry.
The described approach of building the decision tree is performed for every minimal cut set, whereby elements of the solution vectors of previously processed minimal cut sets which are not fixed constraints are used as minimum constraints for further analysis of the minimal cut sets.
In case of Random Tree, a Decision Tree was drawn randomly in which each tree got equal chance to occur in the sampling (Wang et al., 2015).
Building binary decision trees. In order to avoid over-splitting the training data set (and also to overcome the bias of Information gain to overestimate multivalued attributes) we introduced the binarization of continuous and discrete attributes in order to build binary decision trees.
No data transformation is needed since normal distribution is not required for decision trees. An important step is to partition the data into training and validation samples.
Decision trees based on CART algorithms (Breiman, 1993), which are to some degree implemented in rpart R software package (Therneau & Atkinson, 2015), are produced by algorithms that identify various ways of splitting a data set into branch-like segments.
Because random forests are composed of a series of CART (classification and regression tree) decision trees, and vote by the decision tree, the attribute metric used is Gino index [67].
Chen, "Predicting corporate financial distress based on integration of decision tree classification and logistic regression," Expert Systems with Applications, vol.
Glickman, 2011, Selecting Optimal Alternatives and Risk Reduction Strategies in Decision Trees, Operations Research, 59: 631-647.
Quinlan [8] summarizes an approach to synthesizing decision trees that have been used in a variety of systems, and it describes one such system, ID3 in detail.

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