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.
- Glossary > Letter: D
What does "Decision Tree" mean?

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
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