What is a Decision Tree?
Decision trees assist in decision-making or problem-solving by visualizing all potential options, steps, and results in a tree-like diagram. It typically begins with a single question or problem, known as the "root." This root then splits into various choices or conditions, known as "branches." Additional branching occurs based on further choices or conditions, ultimately leading to different endpoints or "leaves" representing the final outcomes or decisions.
- Root node: The starting node of the tree, which represents the main decision to be made.
- Decision nodes: Nodes that represent a decision point. Each decision node has multiple branches, each representing a possible choice.
- Leaf nodes: Nodes that represent the final outcomes of the decision. Each leaf node has a value associated with it, representing the outcome of that decision path.
How It Works
A decision tree begins with a root node (the initial question or condition). Each possible response forms a branch that leads to another node or a terminal outcome. The logic can be built manually using rules and conditions or generated automatically from data. In AI systems, decision trees are used for classification and prediction, while in workflow automation, they act as guided pathways that prompt the right actions or content.
