AIC in machine learning

AIC, or Akaike Information Criterion, is a tool used to evaluate and compare different statistical models to find the best balance between complexity and accuracy. It assesses how well a model fits the data while penalizing overly complicated models that might overfit. A lower AIC value indicates a preferable model that captures the data patterns efficiently without unnecessary complexity. In machine learning, AIC helps select simpler, effective models that generalize well to new data, ultimately improving predictive performance and avoiding overfitting.