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.
McGraw-Hill Dictionary of Scientific & Technical Terms, 6E, Copyright © 2003 by The McGraw-Hill Companies, Inc.

decision tree

A graphical representation of all alternatives in a decision making process.
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In this population, "high risk 2" was defined as (1) previous fasting blood glucose (FBG) levels above 5.6mmol/l or (2) classification as "high risk 1" according to the diabetes decision tree model (Figure 1).
Similarly, 90 samples are collected again as the input for the decision tree.
Decision Tree is a popular classifier that does not require any knowledge or parameter setting.
For mining and processing of image data phase, the study employed decision tree. Digital image principles and decision tree were applied to detect skin diseases using some attributes identified in a digital image of a skin.
In this study, decision tree analysis has examined for the existence of relationships among cage, line, ASM, body weight at sexual maturity (BWSM) on egg production.
A decision tree algorithm pertaining to the student performance analysis and prediction.
We describe early machine learning research in Ljubljana, motivated by medical diagnostic problems, in the areas of building decision trees with Assistant, the development of Naive and Semi-Naive Bayesian classifier and its explanations of individual predictions, and the development of ReliefF and RReliefF algorithms for non-myopic evaluation of attributes in classification and regression, respectively.
In order to predict the probability that a delay incident would occur with flights from one airport to another, conventional data mining (decision tree) and data mining using Bayesian inference were used to build prediction models.
Techniques such as reverse algorithms: decision trees (DT), support vector machines (SVM), neural networks (NN), and genetic algorithms (GA) are among these techniques.
Conventional decision tree rules are generally based on experience and visual interpretation of artificial settings, subject to the influence of subjective factors, and classification and regression tree (Classification And Regression Trees, CART) method can automatically select the classification characteristics and determine the node threshold value.
In terms of the attribute selection method, the decision tree attribute selection method yields better accuracy than the other methods.

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