Baseline Models
Simple reference models used to establish a performance benchmark before evaluating more advanced machine learning models.
What are Baseline Models?
A baseline model provides a straightforward starting point for a machine learning project. It may use a simple statistical method, basic algorithm, or rule-based prediction strategy. Once its performance is measured, teams can compare more sophisticated models against the baseline to determine whether added complexity actually improves accuracy or other important performance metrics.
Why are Baseline Models Important?
Without a baseline, it can be difficult to determine whether a complex model provides meaningful improvement. Baseline models create a clear performance reference, help validate modeling decisions, and prevent teams from investing resources in solutions that offer little additional value.
Common use cases
Baseline models are commonly used in classification, regression, forecasting, model experimentation, benchmarking, and machine learning research.