Dr C.Naveeth Babu

  Decision Tree is a  Supervised learning technique  that can be used for both classification and Regression problems, but mostly it is preferred for solving Classification problems. It is a tree-structured classifier, where  internal nodes represent the features of a dataset, branches represent the decision rules  and  each leaf node represents the outcome. In a Decision tree, there are two nodes, which are the  Decision Node  and  Leaf Node.  Decision nodes are used to make any decision and have multiple branches, whereas Leaf nodes are the output of those decisions and do not contain any further branches. The decisions or the test are performed on the basis of features of the given dataset. It is a graphical representation for getting all the possible solutions to a problem/decision based on given conditions. It is called a decision tree because, similar to a tree, it starts with the root node, which expands on further branches and constructs a tree-like structure. In order to build

 Eye Tracking Technology

  Why was the concept of the circular queue introduced? There was one limitation in the array implementation of  Queue . If the rear reaches to the end position of the Queue then there might be possibility that some vacant spaces are left in the beginning which cannot be utilized. So, to overcome such limitations, the concept of the circular queue was introduced. As we can see in the above image, the rear is at the last position of the Queue and front is pointing somewhere rather than the 0 th  position. In the above array, there are only two elements and other three positions are empty. The rear is at the last position of the Queue; if we try to insert the element then it will show that there are no empty spaces in the Queue. There is one solution to avoid such wastage of memory space by shifting both the elements at the left and adjust the front and rear end accordingly. It is not a practically good approach because shifting all the elements will consume lots of time. The efficient a