A decision tree is a type of supervised machine learning used to categorize or make predictions based on how a previous set of questions were answered
. It is a non-parametric supervised learning algorithm utilized for both classification and regression tasks
. The model is trained and tested on a set of data that contains the desired outcome variable
. Decision trees are particularly useful for data mining and knowledge discovery tasks
. Key features of decision trees include:
- Hierarchical structure : A decision tree has a tree structure consisting of a root node, branches, internal nodes, and leaf nodes
- Branching : The algorithm uses a divide and conquer strategy by conducting a greedy search to split the data into smaller subsets based on the available features
- Simplicity : Decision trees are easy to interpret, with Boolean logic and visual representations making them understandable
- Flexibility : They can handle both classification and regression tasks, as well as missing data values and heterogeneous data types
Decision trees are popular in machine learning due to their intelligibility and ability to visually represent decisions and decision-making processes
. They can be used to clarify risks, objectives, and benefits, making them useful in various environments
