# Validation Split (Edge AI Glossary)

## metadata
- term: Validation Split
- slug: validation-split
- letter: V
- route: /resources/glossary/validation-split
- canonical: https://klyff.com/resources/glossary/validation-split

## Definition & Context
A validation split is the portion of a dataset reserved to evaluate an AI model’s performance during training. Also called a holdout set, it ensures that Edge AI systems can...

## Details
## Validation Split

#### What is Validation Split?

A validation split is the portion of a dataset reserved to evaluate an AI model’s performance during training. Also called a holdout set, it ensures that Edge AI systems can generalize effectively to new, unseen data, reducing overfitting while running on edge devices.

#### Why Is It Used?

Validation splits are essential in Edge AI to monitor model accuracy, detect overfitting, and fine-tune hyperparameters before deployment on constrained devices.

#### How Is It Used?

 
- Split the dataset into training, validation, and test sets (commonly 70/15/15 or 80/10/10).
- Train the model on the training set.
- Evaluate performance using the validation split to adjust parameters.
- Deploy only after confirming robust results on edge devices. 

#### Types of Validation Split

 
- **Random Split:** Randomly divides the dataset into subsets.
- **Stratified Split:** Maintains class distributions across training and validation sets, crucial for unbalanced Edge AI data.
- **Time-Based Split:** Used in sequential data like IoT sensor readings to preserve temporal order. 

#### Benefits of Validation Split

 
- Ensures reliable AI performance on edge devices.
- Reduces overfitting and improves generalization.
- Speeds up model optimization and hyperparameter tuning.