A decision tree is a supervised learning algorithm used for both classification and regression tasks. It partitions the data into subsets based on features, with each node representing a decision point that splits the data.
Use Cases
Credit Risk Assessment
Determining the creditworthiness of applicants based on income, credit history, etc.
Medical Diagnosis
Predicting patient outcomes based on symptoms and medical test results.
Customer Segmentation
Segmenting customers based on purchasing behavior and demographics.
Importance
Interpretability
Easy to understand and visualize, making it useful for explaining decisions.
No Assumptions
This does not require assumptions about the distribution of data.
Handling Non-linear Relationships
Can capture non-linear relationships between features and target variables.
Analogies
A decision tree is like a flowchart where each decision (node) leads to different outcomes (branches). Just as you follow different paths in a flowchart based on decisions, a decision tree makes predictions based on conditions
