Online Calibration
Online calibration is a machine learning technique that continuously adjusts model predictions to maintain accuracy over time as data distributions shift. It involves real-time monitoring and correction of model outputs to ensure they remain calibrated, meaning predicted probabilities accurately reflect true likelihoods. This is crucial for maintaining reliability in dynamic environments where data evolves.
Developers should learn online calibration when building machine learning systems that operate in non-stationary environments, such as recommendation engines, fraud detection, or autonomous vehicles, where data drift can degrade performance. It ensures models adapt to changing patterns without full retraining, reducing maintenance costs and improving trustworthiness in production systems.