INFORMATION GAIN IN DECISION TREE THROUGH GINI INDEX
Best suited for classification and regression problems, decision trees are supervised machine learning algorithms. These algorithms are constructed by breaking down the training data into subsets of output variables of the same class by implementing the particular splitting conditions at each node. The information gain in decision trees is directly formulated through training into a hierarchical structure. The knowledge is holed and displayed by this structure in such a way that it can easily be understood, even by non-experts. While building a decision tree, each node focuses on identifying the attribute and a split condition on that attribute which minimizes the class labels mixing, because pure homogeneous subsets is not possible to achieve consequently giving relatively pure subsets. In this blog, we will discuss the concept of , information gain, gini index and gini index. With the root node of the decision tree, this process of classification begins and ge...