Regression
What is Regression?
Regression is a core machine learning technique used in Edge AI to predict continuous values—like temperature, energy usage, or performance metrics—based on sensor or device data. In Edge computing, regression models run locally on devices, enabling real-time predictions without relying on cloud latency.
Regression in Edge AI is a supervised learning algorithm that maps input variables to a continuous output. It helps devices process and predict outcomes directly at the edge, reducing data transfer and enabling instant decision-making in IoT and AI-driven environments.
Why Is It Used?
Regression is used to help Edge devices learn from real-world data and make predictive judgments—such as forecasting equipment failures, optimizing power consumption, or detecting performance anomalies. It enhances efficiency, accuracy, and responsiveness in decentralized AI systems.
How Is It Used?
At the edge, regression models are deployed on lightweight AI frameworks. Devices process local sensor data to:
Predict future states (e.g., vibration trends in machines)
Enable proactive maintenance
Support adaptive control in IoT systems
This eliminates the need for constant cloud connectivity.
Types of Regression
Linear Regression – Models simple relationships between variables.
Polynomial Regression – Handles nonlinear, complex patterns.
Logistic Regression – Used for binary or categorical predictions.
Ridge/Lasso Regression – Improves model accuracy and reduces overfitting in resource-constrained edge environments.
Benefits of Regression
Low latency predictions for instant insights
Reduced bandwidth costs due to local data processing
Enhanced data privacy and security
Scalable intelligence across distributed devices