![]() ![]() Branch / Sub-Tree: A subsection of the entire tree is called branch or sub-tree.You can say the opposite process of splitting. Pruning: When we remove sub-nodes of a decision node, this process is called pruning.Leaf / Terminal Node: Nodes do not split is called Leaf or Terminal node.Decision Node: When a sub-node splits into further sub-nodes, then it is called the decision node.Splitting: It is a process of dividing a node into two or more sub-nodes.Root Node: It represents the entire population or sample and this further gets divided into two or more homogeneous sets.Important Terminology related to Decision Trees In this case, we are predicting values for the continuous variables. Now, as we know this is an important variable, then we can build a decision tree to predict customer income based on occupation, product, and various other variables. Here we know that the income of customers is a significant variable but the insurance company does not have income details for all customers. Continuous Variable Decision Tree: Decision Tree has a continuous target variable then it is called Continuous Variable Decision Tree.Įxample:- Let’s say we have a problem to predict whether a customer will pay his renewal premium with an insurance company (yes/ no).Categorical Variable Decision Tree: Decision Tree which has a categorical target variable then it called a Categorical variable decision tree.Types of decision trees are based on the type of target variable we have. On the basis of comparison, we follow the branch corresponding to that value and jump to the next node. We compare the values of the root attribute with the record’s attribute. ![]() In Decision Trees, for predicting a class label for a record we start from the root of the tree. ![]()
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