Introduction of Decision Trees

Decision Tree:Overview in different kinds of supervised data mining techniques the¬†Decision Tree are one of the most popular classification and prediction technique. basically the training data samples are organized in form of tree data structure. where the nodes of tree shows the attributes of the data set and edges can be used for demonstrating the values of these attributes. additionally the leaf node of the tree contains the decisions of the Decision Tree algorithms (i.e. decision tree C4.5, ID3, CART). example an example of decision tree is given in figure 1. Figure 1 decision tree example in the above given figure 1 a tree is demonstrated that contains decisions. the decision labels are (yes or no) which is placed in leaf nodes. and nodes of tree (humidity, outlook and wind) are attributes.¬† which are available in data set. because the data set contains both and both the component are help to understand the relationship among the attributes. sometimes these trees can also converted into IF THEN ELSE rules. For above given example a rule can be defined as: IF (Outlook = sun & Humidity = normal) then decision = yes Advantages the following are the key advantages of any decision…

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