Introduction of Decision Trees

Decision Tree:Overview in different kinds of supervised data mining techniques the decision trees 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 classifier. example the decision tree as given in figure 1. Figure 1 decision tree example in the above given figure the decision tree model is demonstrated which contains decisions in terms of yes or no at the leaf nodes. similarly the humidity, outlook and wind are the attributes which are available in data set. additionally the relevant attribute attribute values that are frequently occurred during the evaluation of patterns. sometimes these trees can also used as the IF THEN ELSE rules. from the 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 tree: Decision tree are simple to understand and construct even after a brief exploration….

Insert math as
Additional settings
Formula color
Text color
Type math using LaTeX
Nothing to preview