Statistical Learning Theory

Statistical Learning Theory is a field that explains how computers learn patterns from data to make predictions or decisions. It provides a framework to understand how well a learning algorithm will perform on new, unseen data, based on its performance on known data and the complexity of the model. Essentially, it guides us in balancing model accuracy with simplicity to ensure reliable predictions without overfitting—over-relying on specific data quirks. This theory underpins many machine learning methods used today, helping us build models that generalize well and make accurate, trustworthy predictions in various applications.