![]() A high information gain means that a high degree of uncertainty (information entropy) has been removed. It helps in reducing uncertainty in these trees. Information gain is used in the training of decision trees. In this case, the conditional entropy is subtracted from the entropy of Y. The entropy of the target variable (Y) and the conditional entropy of Y (given X) are used to estimate the information gain. The information gain concept involves using independent variables (features) to gain information about a target variable (class). Information gain is a measure of how uncertainty in the target variable is reduced, given a set of independent variables. ![]() An overview of these fundamental concepts will improve our understanding of how decision trees are built.Įntropy is a metric for calculating uncertainty. Entropy and information gain are the building blocks of decision trees. The information theory can provide more information on how decision trees work. The following diagram shows the three types of nodes in a decision tree. Decision nodes provide a link to the leaves. The nodes in the decision tree represent attributes that are used for predicting the outcome. The leaf node cannot be segregated further. This sequence continues until a leaf node is attained. A decision tree algorithm divides a training dataset into branches, which further segregate into other branches. An overview of decision trees will help us understand how random forest algorithms work.Ī decision tree consists of three components: decision nodes, leaf nodes, and a root node. ![]() A decision tree is a decision support technique that forms a tree-like structure. How random forest algorithm works Understanding decision treesĭecision trees are the building blocks of a random forest algorithm.
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