Training Split
What is Training Split?
A training split is the process of dividing datasets into subsets for training, validation, and testing in machine learning, ensuring Edge AI models learn accurately and perform reliably on-device. Also known as data partitioning, it optimizes model learning and evaluation.
Why Is It Used?
Training splits are essential to prevent overfitting, validate model performance, and ensure that AI models deployed on edge devices operate efficiently and safely in real-world conditions.
How Is It Used?
Data is divided into portions:
Training set: Used to teach the AI model patterns.
Validation set: Used to tune hyperparameters and optimize performance.
Test set: Evaluates the final model’s accuracy on unseen data.
Types of Training Split
Standard split (e.g., 70/15/15) – Most common in Edge AI projects.
K-Fold cross-validation – Rotates subsets to ensure model robustness.
Time-based split – Useful for sequential or IoT streaming data.
Benefits of Training Split
Improves model accuracy on edge devices.
Reduces risk of overfitting and performance bias.
Enables efficient resource usage for on-device computation.
Enhances reliability in real-world AI applications.