decision tree


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decision tree

n
a treelike diagram illustrating the choices available to a decision maker, each possible decision and its estimated outcome being shown as a separate branch of the tree
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The decision tree model was constructed with the CART algorithm using the Orange data mining software package.
In the CART algorithm, pruning option to avoid unnecessary nodes in the decision tree diagram was employed.
Then, a data mining technique, namely Decision Tree Method [6, 13], was used to classify the performance of each key design variable and link them to the overall performance label.
Decision tree method generally used for classification, because it is the simple hierarchical structure.
In recent years some researchers believe that before producing the core decision tree reduced the training data quantity as far as possible compared with after the decision tree produced simplified the decision tree to enhance the performance of decision tree again.
A DCC Smart Fault Decision Tree, enabled by per service, per user data, identifies network related root causes and recommends appropriate corrective actions.
So, the information gain of a feature shows that how important it is for the process of training a decision tree aiming to the different size of CUs.
In the research carried out in [16], two algorithms, decision tree induction (DTI), and a variant of DTI combined with Rough Set are used.
In the current study, considering the groundwater potential relating factors, lithology, lineament density, topology, slope, and river density, decision tree algorithms, C5.
One way to encourage HIV providers to help patients stop smoking is to give them a decision tree that organizes current knowledge about smoke-ending medications.
Common usages of decision tree models include the following:
Decision tree learning is a method for approximating discrete-valued target functions, in which the learned function is represented by a decision tree.

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