R Data Analysis/Decision Trees

• non-linear classifier

• Important Terminologies related to Decision Tree

  • Root Node

    • It represents the entire population or sample.
    • Moreover, it gets divided into two or more homogeneous sets.

* #####Splitting: 
    * In this, we carry out the division of a node into two or more sub-nodes.
* #####Decision Tree: 
    * It is produced when a sub-node splits into further sub-nodes. 
* #####Leaf/Terminal Node: 
    * Nodes that do not split is called Leaf or Terminal node.
* #####Pruning: 
    * When we remove sub-nodes of a decision node, this process is called pruning. It is the opposite process of splitting.
* #####Branch / Sub-Tree: 
    * A subsection of the entire tree is called branch or sub-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.

• Types of Decision Tree

  • Categorical(classification) Variable Decision Tree

    • Decision Tree which has a categorical target variable.

  • Continuous(Regression) Variable Decision Tree

    • Decision Tree has a continuous target variable.

Reference

• R Decision Trees - The Best Tutorial on Tree Based Modeling in R! - DataFlair
• Classification in R Programming: The all in one tutorial to master the concept! - DataFlair