The Nature of Statistical Learning Theory

Statistical Learning Theory is a framework that helps us understand how computers learn from data to make predictions or decisions. It focuses on building models that can generalize well to new, unseen data by balancing complexity and accuracy. The theory provides principles to choose models that avoid overfitting (too perfect on training data but poor on new data) and underfitting (too simple to capture patterns). Essentially, it guides the development of reliable algorithms that learn efficiently from data while maintaining predictive power.