Bayesian Regularization

Bayesian regularization is a technique in machine learning that improves model training by balancing fitting the data well and avoiding overly complex models. It does this by applying principles from Bayesian probability, which treats model parameters as uncertain and updates their estimates based on data. This approach effectively introduces a "penalty" for complexity, helping prevent overfitting—where a model captures noise instead of true patterns. By doing so, Bayesian regularization produces models that generalize better to new data, leading to more reliable and robust predictions.