# What is Overfitting? Definition & Technical Mechanics

> **Source:** [SPIDITS AI AI Glossary](https://spidits.com/ai-glossary)  
> **Category:** Model Training  
> **Canonical Citation:** [https://spidits.com/ai-glossary/overfitting](https://spidits.com/ai-glossary/overfitting)

## Definition
Overfitting is a common training error where a model learns the details and noise in the training dataset to the extent that it negatively impacts its performance on new, unseen test data. The model performs exceptionally well on training data but fails to generalize.

## Architectural Mechanics & Deep Dive
Overfitting is a common training failure mode where a machine learning model learns the training data's noise, outliers, and random fluctuations so perfectly that it fails to generalize to new, unseen testing data. An overfitted model exhibits high training accuracy but poor validation and testing accuracy, requiring regularization techniques like dropout and early stopping.

## Industry Applications & Real-World Use Cases
Validation curve checking, early stopping, and regularized training adjustments

## Frequently Asked Questions

### How do you prevent overfitting?
By using techniques like regularization (L1/L2), dropout, early stopping, cross-validation, and adding more training data.

### What is underfitting?
Underfitting is the opposite; when the model is too simple to learn the relationships in the data, performing poorly on both training and test sets.

## Related Terminology
- [bias-variance-tradeoff](https://spidits.com/ai-glossary/bias-variance-tradeoff)
- [epoch](https://spidits.com/ai-glossary/epoch)

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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)*