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- decision tree diagram 决策树图
- Decision tree is a useful method of classification. 摘要决策树是分类的常用方法。
- The following diagram displays the questions as a decision tree and will help you visualize how the various questions relate to one another. 下面的关系图将这些问题显示为决策树,将帮助您以可视化形式了解不同问题之间的相互关系。
- 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。
- Methods Polymorphic analysis was used for the commercial crude drug Bulbus Lilii with RAPD technique and a tree diagram was constructed. 方法采用RAPD技术对商品药材百合进行了多态性分析，并构建聚类树型图。
- A tree diagram is a graphic representation of a strictly hierarchical structure, which describes a set of elements and their relations to each other. 树状图表是一种具有严谨的等级体系结构的图形表现，它描述了各个相关要素之间的关系。
- 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”。
- Decision trees can be used for prediction. 决策树可用于进行预测。
- 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。
- 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。
- tree diagram of conditional probability 条件概率的树形图
- 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。
- 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。
- 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。
- How to construct the Decision Trees with high precision and small size is core. 如何构造精度高、规模小的决策树是决策树算法的核心内容。
