In today’s lesson, I learned about decision trees, which are visual models of the decision-making process. Imagine them as a flow of queries and choices culminating in a definitive result. The process begins with an initial query, and with each subsequent answer, you proceed along the pathways until you reach the final outcome.
- Root Node: This is the first node of the tree where the data splits. It represents the entire dataset, which then gets divided into two or more homogeneous sets.
- Splitting: It is the process of dividing a node into two or more sub-nodes based on certain conditions.
- Decision Node: When a sub-node splits into further sub-nodes, it’s called a decision node.
- Leaf/Terminal Node: Nodes that do not split any further are called leaves or terminal nodes. They represent the output or the decision taken after computing all attributes.
- Branches or Sub-Trees: A section of the entire tree is called a branch or sub-tree.
- Parent and Child Node: A node, which is divided into sub-nodes, is called the parent node of the sub-nodes, whereas the sub-nodes are the children of the parent node.
- And we done the logistic regression to given data and we written our code.