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 ?
Lin's concordance correlation coefficient (LCCC), coefficient of determination ([R.sup.2]), mean absolute error (MAE) and root mean square error (RMSE) of classification and regression tree (CART), random forest (RF) and random forest with residual kriging (RFRK) Item CART RF RFRK LCCC 0.70 0.80 0.88 [R.sup.2] 0.51 0.70 0.80 MAE 0.44 0.34 0.25 RMSE 0.61 0.48 0.39 Table 5.
During the growth of a regression tree model, the input data domain is recursively divided into subdomains.
In the decision tree algorithm, there are C5.0 algorithm and classification regression tree CART (classification and regression trees) algorithm, and the classification accuracy of CART decision tree algorithm is better than that of C5.0 algorithm and has the advantages of clear structure and so on.
Multitarget Regression Trees. A MTRT was constructed to simultaneously predict FSD and RLCC using canopy reflectance data for each infection level.
The overall BP measurement accuracy, as shown in Figures (6(a) and 6(b)) and Table 2, showed that the regression tree achieved the smallest mean difference of SBP (-0.1 mmHg between reference and estimated SBP) and SD of difference (6.5 mmHg) when compared with the MLR and SVM algorithms.
Weinberger, "Web-search ranking with initialized gradient boosted regression trees," Journal of Machine Learning Research, vol.
Other related classification tools are available, such as classification and regression trees, which use a repeated splitting method.
The regression tree technique is an adaptation of the decision tree for continuous predictions, such as cost.
A regression tree was also used to detect the relationship between body weight and morphometric traits of Uda sheep (Yakubu, 2012).
Stone, Classification and Regression Trees, Wadsworth Advanced Books and Software, Belmont, CA, CRC Press, 1984.
For regression trees, splitting is made in accordance with a squared residuals minimization algorithm, which implies that the expected sum variances in the two resulting nodes should be minimized, as shown in
The RF illustrated in Figure 7 classifies or predicts the value of a variable for an (x) input vector by building a number (K) of regression trees and averaging the results.