# What is Random Forest? Definition & Technical Mechanics

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

## Definition
Random Forest is an ensemble supervised learning algorithm composed of many individual Decision Trees that work together. It trains trees on random subsets of the data and features, averaging their predictions for output.

## Architectural Mechanics & Deep Dive
A random forest is an ensemble learning method used for classification and regression. It constructs a multitude of decision trees during training and outputs the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees, mitigating the overfitting problem of single decision trees.

## Industry Applications & Real-World Use Cases
Financial risk modeling, tabular classification benchmarks, and regression analysis.

## Frequently Asked Questions

### Why does a random forest perform better than a single decision tree?
A single tree is highly prone to overfitting training noise. Random Forest averages predictions across hundreds of trees, canceling out individual errors.

### Is Random Forest a bagging or boosting algorithm?
It is a Bagging (Bootstrap Aggregating) algorithm, as it trains trees in parallel on randomly sampled subsets of the dataset.

## Related Terminology
- [decision-tree](https://spidits.com/ai-glossary/decision-tree)
- [ensemble-methods](https://spidits.com/ai-glossary/ensemble-methods)
- [supervised-learning](https://spidits.com/ai-glossary/supervised-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)*