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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KEYWORDS: Classification, Data mining, Decision tree method, Health expenditure, OECD.
All the tree decision tree algorithms have the ability of being multipurpose tools to allow the phenotypic and genetic characterization of breed traits for sheep breeds.
Gain(A) was used as the splitting criterion in ID3 algorithm [18], which is the foundation of most decision tree generation algorithms.
Although the best decision tree induction algorithms, such as J48, had been developed some time ago, they continue to be regularly used for solving everyday classification tasks [9].
Because the structure of decision tree is simpler more can summarize the thing the rule, expected that the non-leaf node arrives at the descendant node the average way to be always shortest, namely production the average depths of decision tree are smallest, this requests in each node selection good division that uses the information gain in each node of tree as the target of weight node split quality.
A DCC analyses customer complaint data from a CSPs social network account and uses the Smart Fault Decision Tree analysis to identify the root-cause and suggest corrective action.
5, we can observe that most of the CUs with 64x64 pixels have almost been split, even if in some flat regions, in other words, the output of the decision tree of CU with 64x64 pixels is relatively accurate.
The decision tree algorithms [16] are suitable for the multifactor classification problem.
The study involved 100 HIV-positive smokers, 50 randomized to the decision tree group and 50 randomized to standard care.
Decision tree analysis can deal with missing data in two ways: it can either classify missing values as a separate category that can be analyzed with the other categories or use a built decision tree model which set the variable with lots of missing value as a target variable to make prediction and replace these missing ones with the predicted value.
step4 : Construct decision tree and use it to predict efficiency score of other DMUs with the same attribute.

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