Would you believe there are forests in the data? These forests aren’t made up of evergreens or maples, they consist of #decisiontrees.
“A decision tree is a graphical representation of a decision-making process that utilizes a tree-like structure consisting of nodes and branches to model possible outcomes and make predictions.”
The anatomy of decision trees consists of:
- Root node: base of tree
- Splitting: divides the node into sub-nodes
- Decision node: when a subnode is split into more subnodes
- Leaf node: when a subnode stops splitting (represents possible outcome)
- Pruning: When sub-nodes are removed
- Branch: subsection of the tree with multiple nodes
Decision trees provide a visual and intuitive representation of the decision-making process. The structure of the tree enables us to understand and interpret the logic behind each decision and the potential consequences that follow.
Advantages of decision trees include:
- Works for numerical or categorical data
- Models problems with multiple outcomes
- Requires less data than other data modeling techniques
Disadvantages of decision trees include:
- Not ideal for large data sets
- Can become complex when dealing with uncertainty
- Affected by noise in data
