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Decision tree is a useful method of classification. 摘要决策树是分类的常用方法。
A decision tree is a graphic model of a decision process. 决策树是描述决策过程的一种图形。
From the viewpoint of STACKELBERG decision problems, a leader-follower hierarchical decision model is proposed for distributed restoration after blackout. 引入主从递阶决策概念,建立了一种适合于大面积停电后分布式恢复的主从递阶决策模型。
Engrafting hierarchical analysis method with both-fuzzy hierarchical decision, this paper presents both-branch fuzzy hierarchical analysis model, and gives its algorithm. 把层次分析的方法与双枝模糊层次决策相互嫁接，相互渗透，提出双枝模糊层次分析模型，并给出双枝模糊层次分析的算法。
In the Grid pane, click Source and then select TM Decision Tree mining model. 在“网格”窗格中,单击“源”,然后选择“TM Decision Tree挖掘模型”。
Click Select Model, expand Targeted Mailing, and then choose TM Decision Tree. 单击“选择模型”,展开“目标邮件”,再选择TM Decision Tree。
This viewer contains two tabs, Decision Tree and Dependency Network. 此查看器包含两个选项卡,即“决策树”和“相关性网络”。
Evolutionary decision tree method has the advantage of global search. 演化决策树方法将传统的决策树算法与演化算法相结合,具有全局搜索的优点。
Decision tree, neural networks and Bayesian networks are the main tools of KDD. 决策树、神经网络、Bayesian网络等是当前知识发现的重要工具。
Decision trees can be used for prediction. 决策树可用于进行预测。
By using of both-branch fuzzy decision theory and hierarchical analysis method, this paper puts forward both-branch fuzzy hierarchical structure, and presents both-branch fuzzy hierarchical decision model. 利用双枝模糊决策理论与层次分析的方法，本文提出双枝模糊层次结构，给出双枝模糊层次决策模型。
For example, in a decision tree mining model the viewer will use Cyan to display continuous attributes. 例如,在树挖掘模型中,查看器将使用青色来显示连续属性。
group hierarchical decision making 群体层次决策
On the Decision Tree tab, you can examine all the tree models that make up a mining model. 在“决策树”选项卡上,可以检查构成挖掘模型的所有树模型。
When you build a decision tree model, Analysis Services builds a separate tree for each predictable attribute. 生成决策树模型时,Analysis Services将为每个可预测属性生成一个单独的树。
Now there are many methods that has been applied to this field, such as SVM, KNN, Naive Bayes, Decision Tree, etc. 目前已经有许多方法应用到该领域。如支持向量机方法(SVM)、K近邻方法(KNN)、朴素贝叶斯方法(Naive Bayes)、决策树方法(Decision Tree)等等。
The traditional decision tree category methods(such as:ID3,C4.5) are effective on small data sets. 传统的决策树分类方法(如ID3和C4.;5)对于相对小的数据集是很有效的。
This paper regards decision tree as basic classifier, and introduces the least square technology for linear fusion. 以决策树作为基本分类器，引入最小二乘技术进行多分类器线性融合。
Two methods for text categorization fuzzy rule extraction are presented based on fuzzy decision tree. 本文提出了两种基于模糊决策树的模糊文本分类规则抽取方法。
One of the best ways to analyze a decision is to use so-called decision trees. 所谓决策树是进行决策分析的最佳方法之一。