For a class, every branch from the root of the tree to a leaf node having the same class is conjunction (product) of values, different branches ending in that class form a disjunction (sum). The Sum of product (SOP) is also known as Disjunctive Normal Form. Order to placing attributes as root or internal node of the tree is done by using some statistical approach.ĭecision Trees follow Sum of Product (SOP) representation.Records are distributed recursively on the basis of attribute values.If the values are continuous then they are discretized prior to building the model. Feature values are preferred to be categorical.In the beginning, the whole training set is considered as the root.This process is recursive in nature and is repeated for every subtree rooted at the new node.īelow are some of the assumptions we make while using Decision tree: Parent and Child Node: A node, which is divided into sub-nodes is called a parent node of sub-nodes whereas sub-nodes are the child of a parent node.ĭecision trees classify the examples by sorting them down the tree from the root to some leaf/terminal node, with the leaf/terminal node providing the classification of the example.Įach node in the tree acts as a test case for some attribute, and each edge descending from the node corresponds to the possible answers to the test case.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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