# What is Decision Tree? Definition & Technical Mechanics

> **Source:** [SPIDITS AI AI Glossary](https://spidits.com/ai-glossary)  
> **Category:** Foundational AI  
> **Canonical Citation:** [https://spidits.com/ai-glossary/decision-tree](https://spidits.com/ai-glossary/decision-tree)

## Definition
A Decision Tree is a non-parametric supervised learning method used for both classification and regression. The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features.

## Architectural Mechanics & Deep Dive
A decision tree is a non-parametric supervised learning algorithm used for both classification and regression tasks. It structures decisions in a flowchart-like tree structure, where each internal node represents a test on an input feature, each branch represents the outcome of the test, and each leaf node represents a class label or a continuous value. Decision trees are highly interpretable, but they are prone to overfitting unless pruned or combined into ensemble methods like random forests.

## Industry Applications & Real-World Use Cases
Customer churn prediction, medical diagnosis tree diagrams, and baseline classification.

## Frequently Asked Questions

### What is a node in a decision tree?
A split condition based on a feature value. The leaves represent final target outcomes or classifications.

### What is the main drawback of decision trees?
They are highly prone to overfitting the training data, which is typically resolved by pruning the tree or using random forests.

## Related Terminology
- [supervised-learning](https://spidits.com/ai-glossary/supervised-learning)
- [random-forest](https://spidits.com/ai-glossary/random-forest)
- [machine-learning](https://spidits.com/ai-glossary/machine-learning)

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*From the SPIDITS AI Knowledge Base (336+ terms indexed). Explore full technical definitions: [https://spidits.com/ai-glossary](https://spidits.com/ai-glossary)*