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Important Terminologies Related To Decision Trees In Data Science
Decision Trees are one of the prominent techniques in Data Science that helps the analysts in making accurate data-driven smart decisions. To better explain the prominence of Decision Trees, let’s consider the following example.
A company needs to predict whether a customer would or wouldn’t renew his premium with an insurance company. Customer income will be playing the role of significant variable in this case but is more unlikely that the insurance company would be getting the income details of its customer. So, in such a case, Decision Tree is developed based on occupation, product, and various other variables. This means that we will be predicting the values for continuous variables.
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Now, let’s look at some of the important terminologies that are used in Decision Trees in Data Science.
- Root Node
A Root Node generally represents the entire sample & would get divided into two or more homogeneous sets.
The division of a single node into two or more sub-nodes is known as Splitting process.
- Decision Node
Whenever a sub-node gets splits into multiple sub-nodes then it is referred to as a Decision Node.
- Leaf or Terminal Node
Nodes that don’t get split are referred to as called Leaf or Terminal node.
The process of removing sub-nodes of a decision node is referred to as pruning.
- Branch (or) Sub-Tree
A subsection of the entire tree is called branch or sub-tree.
- Parent & Child Node
The node that gets divided into sub-nodes is called a parent node & the sub-nodes are referred to as the child of a parent node.
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