Generalization of Machine Learning models - Concepts (part 2)

Transitioning to the second part of our discussion on generalization, we delve deeper into refining the concepts from the initial segment. Here, we explore the strategic approach to training machine learning models with the goal of minimizing generalization error. This entails striking a harmonious balance between model complexity and adaptability. Central to this phase is the introduction of a crucial technique: cross-validation. This method serves as a cornerstone in our endeavor to minimize error and enhance model fitness. By systematically partitioning data and iteratively training on different subsets while evaluating on others, cross-validation provides a robust framework for both fine-tuning models and gauging their ability to generalize effectively. This session equips us with a sophisticated methodology for optimizing our models’ performance while remaining attuned to the overarching goal of accurate prediction and generalization.