What does "Decision Tree" mean?

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

Where can you find this term?

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