![]() ![]() Let’s take a look at the below image on two nodes with different impurities. By the above, we can say the information gain in node 1 is higher.īy the above, we can say the balanced nodes or most impure nodes require more information to describe. We can say that in node 1 we are required more information than the other nodes to describe a decision. By this above, we can say that in node three we don’t need to make any decision because all the instances are representing the direction of the decision in the class first side wherein in node 1 there are 50% chances to decide the direction of both classes. To understand the information gain let’s take an example of three nodesĪs we can see in these three nodes we have data of two classes and here in node 3 we have data for only one class and similarly, we have less data for the second class than the first class in node 2, and node 1 is balanced. The information gained in the decision tree can be defined as the amount of information improved in the nodes before splitting them for making further decisions. To perform a right split of the nodes in case of large variable holding data set information gain comes into the picture. In the above example, we have only two variables which are very basic and easy for us to understand where and which node to split. And split on the nodes makes the algorithm make a decision. The rectangles in the diagram can be considered as the node of the decision tree. In this example, the decision tree can decide based on certain criteria. Where a student needs to decide on going to school or not. The above diagram is a representation of the workflow of a basic decision tree. Unlike linear algorithms, decision trees algorithms are capable of dealing with nonlinear relationships between variables in the data. so basically the decision tree algorithm is a supervised learning algorithm that can be used in both classification or regression analysis. Steps to Split Decision Tree using Information Gainīefore proceeding further, let us have a quick understanding of the decision tree so that we can make a fundamental for our main discussion.īefore going deep into the main concept of the article let us have a basic introduction of the decision tree.The major points to be covered in the article are listed below In this article, we will have an in-depth understanding of how information gain is used with a decision tree with a real-life example. Information gain is one of such criteria that is used to construct the decision trees based on the training features. The reason behind this benefit is that it uses different criteria to construct the trees by fully focusing on the features of the training data. When it comes to giving special considerations to the features to be used for modelling purposes, the decision tree is the best-suited algorithm in this case. ![]() Decision trees are one of the classical supervised learning techniques used for classification and regression analysis. ![]()
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