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Introduction of Decision Trees

Decision Tree : Overview Data mining techniques that help to make decisions using the available facts can be termed as Decision Tree. Decision trees are not only useful for decision making applications it is also used for classification and prediction task. There are some popular decision Tree algorithms namely C4.5, ID3, and CART. These algorithms are supervised learning algorithms. During training, the input sample patterns are represented as tree data structure. example An example of decision tree is given in figure 1, where nodes of tree shows the attributes of the data set. Additionally edges relationship among two nodes using the values of available attributes. The leaf node of the tree is recognized as decision node. Figure 1 decision tree example Figure 1 demonstrates a decisions tree. Here decision labels are (yes or no), it is also known as class labels. In decision trees the class label is placed on leaf nodes. Additionally the nodes humidity, outlook and wind are attributes in data-set. Because the data set contains decision and attributes and decision tree graphically represent the data. Therefore it is helpful to understand the relationship among them. Sometimes the decision trees are used in form of IF THEN ELSE rules. An example…