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.