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Decision tree algorithm short Weka tutorial Machine Learning : brief summaryDanilo, CroceBasili, Roberto
This tutorial explains WEKA Dataset, Classifier, and J48 Algorithm for Decision Tree. Also provides information about sample ARFF datasets for Weka: In theprevious tutorial, we learned about the Weka Machine Learning tool, its features, and how to download, install, and use Weka Machine Learning ...
In Weka, the decision tree analysis is in the Classify tab page (Figure 4), and the decision tree algorithm can be selected by clicking the Choose option on the Classify tab page. In the options of the decision tree algorithm of Weka, the J48 algorithm is the C4.5 decision tree algorithm...
The classification rules of the decision tree are easy to understand, and the hardware implementation of the model is simple because it can be implemented in an if-then-else way. This paper uses the J48 algorithm in WEKA [20], which is an open-source machine learning framework, to train ...
The Decision Tree Algorithm A decision tree is a flowchart-like tree structure where an internal node represents a feature(or attribute), the branch represents a decision rule, and each leaf node represents the outcome. The topmost node in a decision tree is known as the root node. It learns...
Classification And Regression Tree(CART)也是决策树的一种,并且是非常重要的决策树。除去上文提到的C4.5外,CART算法也在Top Ten Machine Learning Algorithm中,可见决策树的重要性和CART算法的重要性。 CART的特性主要有下面三个,其实这些特性都不完全算是CART的特性,只是在CART算法中使用,并且作为算法的重要基础: ...
1:最优Decision Tree是NP难题,所以使用的Decision-Tree算法都是基于启发式(Heuristic)算法,如Greedy Algorithm等,在每个节点判断都是根据局部最优解来进行操作。启发式算法不能保证返回全局最优的Decision Tree。 2:容易产生过于复杂的树,不能很好地获得数据的通用模型,这个实际上是被称为是Overfitting,剪枝技术能够很好...
The Decision Tree Algorithm A decision tree is a flowchart-like tree structure where an internal node represents a feature(or attribute), the branch represents a decision rule, and each leaf node represents the outcome. The topmost node in a decision tree is known as the root node. It learns...
We applied the "Decision Tree" technique and "J48" algorithm in the WEKA (3.6.10 version) software to develop the model. After data preprocessing and preparation, we used 22,398 records for data mining. The model precision to identify patients was 0.717. The age factor was placed in the ...